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979 results for “image dataset”
Imaging and multi-omics datasets converge to define different neural progenitor origins for ATRT-SHH subgroups [scRNAseq_ATRT_CAL]
GEO Series GSE241737. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Dataset of A2780 and G361 cells - efect of FITC phototoxicity, quantitative phase imaging (2/2)
<p>Part of article Feith, M,Vičar, T., Gumulec, J., Raudenská, M. Wingren, AG, Masařík, M., Balvan, J. Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light, <em>Appl. Sci.</em> <strong>2020</strong>, <em>10</em></p> <p>Increased exposition to blue light may induce many changes in cell behavior and significantly affect the critical characteristics of cells. Here we show that multimodal holographic microscopy (MHM) within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. We focused on the effect of blue light with a wavelength of 485 nm on morphological and dynamical parameters of four cell lines, malignant PC-3, A2780, G361 cell lines, and the benign PNT1A cell line. We used MHM with blue light doses 24 mJ/cm<sup>2</sup>, 208 mJ/cm<sup>2 </sup>and two kinds of expositions (500 and 1000 ms) to acquire real-time quantitative phase information about cellular parameters. It has been shown that specific doses of the blue light significantly influence cell motility, cell dry mass and cell density. These changes were often specific for the malignant status of tested cells. Blue light dose 208 mJ/cm<sup>2 </sup>× 1000 ms affected malignant cell motility but did not change the motility of benign cell line PNT1A. This light dose also significantly decreased proliferation activity in all tested cell lines but was not so deleterious for benign cell line PNT1A as for malignant cells. Light dose 208 mJ/cm<sup>2 </sup>× 1000 ms oppositely affected cell mass in A2780 and PC-3 cells and induced different types of cell death in A2780 and G361 cell lines. Cells obtained the least damage on lower doses of light with shorter time of exposition.</p> <p><strong>Materials and Methods </strong></p> <p><em>Cell Lines</em></p> <p>The PC-3, A2780, PNT1A, and G361 cell lines were purchased from HPA Culture Collections (Salisbury, UK). PC-3 prostate cancer cell line was derived from bone metastasis of a 4-grade prostatic adenocarcinoma of a 62-year-old Caucasian male The A2780 cell line was derived from the ovarian carcinoma of a nontreated patient according to ECACC. PNT1A cell line was established from prostatic epithelial tissue of healthy 35-years old male and immortalized by plasmid transfection containing the SV40 genome with defective replication origin. The G361 cell line was established from a malignant melanoma of a 31-year-old male Caucasian. The G361 cells produce melanin for up to 50 population doublings. As the aim of this study is to compare the effect of blue light on the cell lines differing by morphology, transformation state, sensitivity to cell death, and origin, we decided to use the cell lines listed above. PC-3 cells are larger in comparison with small A2780 cells. Benign PNT1A cell line differs from malignant PC-3, and all four cell lines are derived from diverse tissues of origin. Furthermore, melanoma G361 cells expressing melanin may differ in the reaction of cells to blue light exposure.</p> <p></p> <p><em>Cell Cultivation</em></p> <p>All four cell lines were cultivated in 25 cm<sup>2</sup> flasks with 5 ml of media at 37 °C in a humidified incubator (60%) with 5% CO<sub>2 </sub>(Sanyo, Osaka City, Japan). Cell lines A2780, PNT1A and G361 were cultured in RPMI-1640 medium with phenol red indicator, L–glutamine, FBS and antibiotics penicillin/streptomycin (Sigma Aldrich Co., St. Louise, MO, USA). For the PC-3 cell line cultivation, Ham´s F-12 medium with FBS and antibiotics (Sigma Aldrich Co., St. Louis, MO, USA) was used. The same supplementation with antibiotics (penicillin 100 U/mL and streptomycin 0.1 mg/mL) and 10% FBS was used in both media. The cell medium was changed two times per week. Cell subculturing was done with 10% of trypsin solution (PAA, Pasching, Austria) with previous washing with EDTA (0.02% in PBS buffer).</p> <p><em>QPI and Holographic Microscopy and Fluorescence Setting</em></p> <p>QPI was performed by using a Q-PHASE multimodal holographic microscope (Telight, Brno, CZ). The Q-PHASE is equipped with fluorescence module using a halogen lamp as a non-coherent source of blue light. In this work, the module was used as a source of blue light for treatment of observed cell lines. The 485 nm light waves are emitted by the fluorescence light source of the attached module. Before the imaging experiment, cells were cultivated overnight in a concentration of 7000 cells/mL in flow chamber µ-Slide I Lauer Family (Ibidi, Martinsried, Germany). During the measurements, the chamber with cells was incubated in 37 °C humidified, 5% CO<sub>2</sub> atmosphere in H201–for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). Images and holograms were captured with lens Nikon Plan 10/0.3 and CCD camera (XIMEA MR4021 MC-VELETA, Münster, Germany) respectively. The fluorescence mode used was a plasma light source (Sutter Instrument Lambda XL Novato, CA, USA). Cells were irradiated with a 485 nm light with a 25 nm bandwidth. Light doses 0 mJ/cm<sup>2</sup>, 24 mJ/cm<sup>2</sup> and 208 mJ/cm<sup>2 </sup>were achieved by the combination of time exposition and light intensity.</p> <p>The images were acquired automatically from seven positions every 3 min for 24 h. Holographic images were collected by custom software and raw data were numerically reconstructed. The numerical reconstruction was performed by custom software where the established methods of the fast Fourier-transform and phase unwrapping are implemented. The output from the software is an unwrapped phase image. This image has high intrinsic contrast and can be processed by an available image processing software. The unwrapped phase image is integrated phase shift through the cell and it is proportional to integrated cell dry mass density.</p>
Image datasets for jammer classification in GNSS
<p>Set of spectrogram binary images corresponding to GNSS signal with and without interference in a variety of scenarios and signal parameters. It also includes two Matlab functions to perform the analisys. </p>
Datasets corresponding to publication: AIDeveloper: deep learning image classification in life science and beyond
<p>Datasets and videos corresponding to publication:<br> AIDeveloper: deep learning image classification in life science and beyond</p>
Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis
Open the record for dataset details and reuse information.
Thermal Image Dataset for Concealed Handgun Detection
Open the record for dataset details and reuse information.
Imaging dataset acquired by PCAT
<p>The "concave" and "convex" contain 8*8 time-series signals of ultrasonic bulk wave array inspection by the 8-element PCAT with concave and convex surfaces, respectively. The "resonance" contains frequency spectra of the ultrasonic local resonances excited by a single PCAT element with scanning lateral distance. The imaging algorithms are easy to reproduce. Other data will be made available on request.</p>
Untreated leaf image dataset
<p>A total of 479 leaves of five species (99 <em>Quercus acutissima</em>, 166 <em>Zelkova serrata</em>, 77 <em>Prunus × yedoensis</em>, 49 <em>Morella rubra</em>, and 88 <em>Ficus erecta</em>) were sampled at the Ito Campus of Kyushu University from August 2021 to November 2021.</p>
The datasets for Precise immunofluorescence canceling enables highly multiplexed imaging
<p>Cell states are regulated by responsive signal pathways upon ligand-binding to the receptor and inter-cellular interactions. Thus, high-resolution imaging has been attempted to explore the dynamics of signaling. Recently, multiplexed imaging has been introduced to profile cell state by acquisition of comprehensive spatial protein information in the cells. However, it is still challenging to compromise resolution for visualizing activated signals. Here we developed ‘Precise Emission Canceling Antibody (PECAb)’ attached with erasable fluorescent labeling. PECAbs allow high-resolution sequential imaging using 206 antibodies and it allowed reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system serves as a comprehensive platform for analyzing complex cell processes.</p>
MedIMeta: A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset
<p>We introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset designed to facilitate the development and standardised evaluation of ML models and cross-domain few-shot learning algorithms for medical image classification. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, offering opportunities for both single-task and multi-task training. All tasks are standardised to the same format and readily usable in PyTorch or other ML frameworks. All datasets have been previously published with an open license that allows redistribution or we obtained an explicit permission to do so.</p> <p>Each dataset within the MedIMeta dataset is standardized to a size of 224 × 224 pixels which matches image size commonly used in pre-trained models. Furthermore, the dataset comes with pre-made splits to ensure ease of use and standardized benchmarking. We release a user-friendly Python package to directly load images for use in PyTorch.<br><br></p> <h3>Links</h3> <ul> <li>Project website: <a href="https://www.woerner.eu/projects/medimeta/" target="_blank" rel="noopener">https://www.woerner.eu/projects/medimeta/</a></li> <li>Data loading code (medimeta Python package): <a href="https://github.com/StefanoWoerner/medimeta-pytorch" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-pytorch</a></li> <li>Data creation code: <a href="https://github.com/StefanoWoerner/medimeta-dataset-scripts" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-dataset-scripts</a></li> </ul> <p> </p> <h3>Dataset Overview</h3> <table> <tbody> <tr> <td><strong>Dataset Name</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>License</strong></td> <td><strong>Domain</strong></td> <td><strong>Task Names</strong></td> <td><strong>Task Targets</strong></td> <td><strong># Labels</strong></td> </tr> <tr> <td>AML Cytomorphology</td> <td>aml</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>morphological class</td> <td>multi-class classification</td> <td>15</td> </tr> <tr> <td>Breast Ultrasound</td> <td>bus</td> <td>CC BY-SA 4.0</td> <td>Breast ultrasound</td> <td>case category<br>malignancy</td> <td>multi-class classification<br>binary classification</td> <td>3<br>2</td> </tr> <tr> <td>Colorectal Cancer Histopathology</td> <td>crc</td> <td>CC BY-SA 4.0</td> <td>Histopathology</td> <td>tissue class</td> <td>multi-class classification</td> <td>9</td> </tr> <tr> <td>Chest X-ray Multi-disease</td> <td>cxr</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>disease labels<br>patient sex</td> <td>multi-label classification<br>binary classification</td> <td>14<br>2</td> </tr> <tr> <td>Dermatoscopy</td> <td>derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>disease category</td> <td>multi-class classification</td> <td>7</td> </tr> <tr> <td>Diabetic Retinopathy (Regular Fundus)</td> <td>dr_regular</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level<br>Overall quality<br>Artifact<br>Clarity<br>Field definition</td> <td>ordinal regression<br>binary classification<br>ordinal regression<br>ordinal regression<br>ordinal regression</td> <td>5<br>2<br>6<br>5<br>5</td> </tr> <tr> <td>Diabetic Retinopathy (Ultra-widefield Fundus)</td> <td>dr_uwf</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level</td> <td>ordinal regression</td> <td>5</td> </tr> <tr> <td>Fundus Multi-disease</td> <td>fundus</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>disease presence<br>disease labels</td> <td>binary classification<br>multi-label classification</td> <td>2<br>45</td> </tr> <tr> <td>Glaucoma-specific fundus images</td> <td>glaucoma</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>Glaucoma suspect</td> <td>binary classification</td> <td>2</td> </tr> <tr> <td>Mammography (Calcifications)</td> <td>mammo_calc</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology<br>calc type<br>calc distribution</td> <td>binary classification<br>multi-label classification<br>multi-label classification</td> <td>2<br>14<br>5</td> </tr> <tr> <td>Mammography (Masses)</td> <td>mammo_mass</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology<br>mass shape<br>mass margins</td> <td>binary classification<br>multi-label classification<br>multi-label classification</td> <td>2<br>8<br>5</td> </tr> <tr> <td>OCT</td> <td>oct</td> <td>CC BY-SA 4.0</td> <td>OCT</td> <td>disease class<br>urgent referral</td> <td>multi-class classification<br>binary classification</td> <td>4<br>2</td> </tr> <tr> <td>Axial Organ Slices</td> <td>organs_axial</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Coronal Organ Slices</td> <td>organs_coronal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Sagittal Organ Slices</td> <td>organs_sagittal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Peripheral Blood Cells</td> <td>pbc</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>cell class</td> <td>multi-class classification</td> <td>8</td> </tr> <tr> <td>Pediatric Pneumonia</td> <td>pneumonia</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>pneumonia presence<br>disease class</td> <td>binary classification<br>multi-class classification</td> <td>2<br>3</td> </tr> <tr> <td>Skin Lesion Evaluation (Dermoscopy)</td> <td>skinl_derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>Diagnosis<br>Diagnosis grouped<br>Pigment Network<br>Blue Whitish Veil<br>Vascular Structures<br>Vascular Structures grouped<br>Pigmentation<br>Pigmentation grouped<br>Streaks<br>Dots and Globules<br>Regression Structures<br>Regression Structures grouped</td> <td>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification</td> <td>15<br>5<br>3<br>2<br>8<br>3<br>5<br>3<br>3<br>3<br>4<br>2</td> </tr> <tr> <td>Skin Lesion Evaluation (Clinical Photography)</td> <td>skinl_photo</td> <td>CC BY-SA 4.0</td> <td>Clinical skin imaging</td> <td>Diagnosis<br>Diagnosis grouped<br>Pigment Network<br>Blue Whitish Veil<br>Vascular Structures<br>Vascular Structures grouped<br>Pigmentation<br>Pigmentation grouped<br>Streaks<br>Dots and Globules<br>Regression Structures<br>Regression Structures grouped</td> <td>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification</td> <td>15<br>5<br>3<br>2<br>8<br>3<br>5<br>3<br>3<br>3<br>4<br>2</td> </tr> </tbody> </table>
Receiver Function Image of the Mantle Transition Zone beneath Western China: Fragmented Subduction and Counterflow Upwelling --- Result Dataset
<p><span>The compressed file contains the 3-D mantle transition zone imaging results of western China and its neighboring regions, including the depths of the 410- and 660-km discontinuities and the MTZ thicknesses.</span></p>
Images Dataset for the Work of Phase Separation-based Antiviral Decoy Particles as Basis for Programmable Broad-spectrum Therapeutics
<p>Images dataset for the work of Phase Separation-based Antiviral Decoy Particles as Basis for Programmable Broad-spectrum Therapeutics.</p> <p>Used for the code cited in the paper.</p>
Dataset of DICOM and MatLab images for review purposes relative to JAPPL-00449-2018
<p>See rebuttal note.</p>
The effect of spatial energy spread on sound image size and speech intelligibility [dataset]
<p>This dataset contains the data and supplementary material for the publication titled "The effect of spatial energy spread on sound image size and speech intelligibility" published in the Journal of the Acoustical Society of America.</p> <p>This repository contains the results from the three experiments as well as a figure showing the adaptive tracks of the adaptive procedure and the estimated psychometric functions in experiment 3. The subject identifiers are common across the three experiments.</p> <p><strong>Experiment 1: </strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, audio/stimulus type, spatial location of stimulus, room condition, repetition</em></p> <p>Measured variables: <em>Source image size, source direction (azimuth), source distance</em></p> <p><strong>Experiment 2:</strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, interferer location, room condition</em></p> <p>Measured variable: <em>Speech reception threshold (SRT)</em></p> <p><strong>Experiment 3:</strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, repetition</em></p> <p>Measured variable: <em>Speech reception angle (SRA)</em></p> <p>The figure (exp3_adaptiveTracks.png) shows the adaptive tracks in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the trial number and the y-axis the separation angle between target and interfering talkers. Each color indicates one subject.</p> <p>The figure (exp3_psychometricFcns.png) shows the estimated psychometric functions in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the separation angle between target and interfering talkers and the y-axis is the percent correct words. Each color indicates one subject. The black line indicates the median over the subjects and repetitions and the black cross the SRA predicted with this method.</p>
Wild Berry image dataset collected in Finnish forests and peatlands using drones
<p>Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands and under the canopy of Finnish forests using drones. Unlike previous and related datasets, WildBe includes new varieties of berries, such as bilberries, cloudberries, lingonberries, and crowberries, captured under severe light variations and in cluttered environments.</p>
Dataset for receiver function imaging of an east-west trending GDS30 array in southern Tibet
<p>This dataset contains the receive function traces and associated teleseimic waveforms used in a submitted manuscript for peer review. Details can be found in the readme.txt file.</p>
Sound field image with objects dataset
<h2>Description</h2> <p>This sound field image with objects dataset contains clean-noisy pairs of complex-valued sound-field images generated by 2D acoustic simulations. The dataset was initially prepared for SoundSil-DS (https://github.com/nttcslab/soundsil-ds), a DNN-based denoising method for optically measured sound fields. Please check our GitHub repository and paper for details.</p> <h2><br>Directory structure</h2> <p>The dataset contains three directories: training, validation, and evaluation. Each directory contains "soundsource#" sub-directories (# represents the number of sound sources used in the acoustic simulation). Each sub-directory has three h5 files for data (clean, white noise, and object-mask) and one CSV file listing random parameter values used in the simulation.</p> <p>- /training</p> <p> - /soundsource#</p> <p> - sf_true.h5</p> <p> - sf_noise_white.h5</p> <p> - mask_data.h5</p> <p> - random_variables.csv</p>
Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper
<p>Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper (https://arxiv.org/abs/1911.12291)</p>
Mouse skull nuclei dataset for microscopy image denoising benchmark as used in PPN2V paper
<p>Mouse skull nuclei dataset for microscopy image denoising benchmark as used in PPN2V paper (https://arxiv.org/abs/1911.12291)</p>
Image_Annotation_Datasets
<p>Image_Annotation_Datasets</p>
ScienceDex guides
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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.