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Dataset results
469 results for “Image analysis”
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper
<p>This dataset contains:</p> <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>
Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
<p>Hyperspectral imaging can capture information beyond conventional RGB cameras; thus, it has many applications, such as material identification and spectral analysis. However, like many camera systems, most of the existing hyperspectral cameras are still passive imaging systems: they require external light sources to illuminate the objects to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting, and the imaging system cannot function in a dark or low-lighting environment. This work develops a prototype system for active hyperspectral imaging, which actively emits different single-wavelength lights at different frequencies when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is normalized to extract reflectance information; second, the system is capable of collecting information at the desired spectral range by tailoring the light sources; third, an active system is mechanically easier to make, since it does not require complex band filters as used in passive systems; last, such a system may work under low light or dark environments, which greatly facilitate underground/subsurface sensing applications such as borehole based mining exploration. This prototype is achieved by using an array of low-cost and single-wavelength LED (Light Emitting Diode) lights, a remote control module controlling the LED illuminator, and the shutter of a full spectrum camera. We demonstrate that such design is feasible and could yield informative hyperspectral images for spectral analysis and machine learning-based object identification in low light or dark environments, having great potential to benefit both the academic and industry such as in geochemistry, earth science, subsurface energy, and mining.</p>
Galaxy Training Data for "End-to-End Tissue Microarray Image Analysis with Galaxy-ME"
<p>This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by <a href="https://pubmed.ncbi.nlm.nih.gov/34824477/">Schapiro <em>et al</em></a>.</p>
Chapter 11 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 11 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 6 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 6 of the book, "Data Science for Nano Image Analysis"</p>
Chapter 5 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 5 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 8 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 8 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 4 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 4 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 3 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 3 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 2 Image Data of the book "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 2 of the book, "Data Science for Nano Image Analysis", by Park and Ding, 2021, Springer Nature</p>
Chapter 7 Image set for the book, "Data Science for Nano Image Analysis"
<p>This is the image set used in Chapter 7 of the book, "Data Science for Nano Image Analysis".</p>
Chapter 10 Image set for the book, "Data Science for Nano Image Analysis"
<p><br> This is the image set used in Chapter 10 of the book, "Data Science for Nano Image Analysis".</p>
Imaging Data for: Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis
<p>This dataset contains the microfluidic microscopy time-lapse data for the publication <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">"Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis"</a>.</p> <p>The imaging data is recorded as raw 16-bit tif-stacks. The sequences <span>17406 - 17410 and 17411 - </span><span>17415 contain the aerobic and anaerobic conditions, respectively. For details about cultivation conditions and image processing please have a look into our <a href="http://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">paper</a> or the public code repository <a href="https://github.com/JuBiotech/Supplement-to-Kasahara-et-al.-2023a">Supplement-to-Kasahara-et-al.-2023a</a>.</span></p>
Original data for "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" article
<p>This upload includes both original and supplementary data for the publication "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" by R. Li, A. Makogon, T. Galochkina, J-F. Lemineur, F. Kanoufi, and V. Shkirskiy, published in the Small Methods journal. The preprint version of the paper is available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2023-sgvt0">https://doi.org/10.26434/chemrxiv-2023-sgvt0</a>).</p> <p>The file "Data_processing.zip" contains the original optical image of the interface, maps of film evolution rates in acidic and salt environments, COMSOL data output in acidic and salt environments, as well as a Jupyter Lab file that demonstrates how to process this data.</p> <p>The file "Comparison_of_SEM_images.zip" includes the original SEM images used in the current study and in our previous work (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v3">https://doi.org/10.26434/chemrxiv-2022-rn77b-v3</a>), along with a Jupyter Lab file that illustrates the data processing procedure.</p> <p>The file "Original_data_in_npy_format.zip" contains all original data from SEM/EDX and RM experiments.</p> <p>We recommend opening the Jupyter Lab files in a Python 3 environment. PDF files in the root directory provide outputs of all uploaded Jupyter Lab files.</p>
Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging. (Histology Images)
<p>These are the histology images on "Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging."</p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Dual-Channel Near-Infrared Autofluorescence Imaging and AI Analysis to Locate Parathyroid Glands (PTFinder)
ClinicalTrials.gov study NCT07010380. IPD Sharing: YES. Countries: 1. Publications: 1.
Analysis of 18F-AV-1451 PET Imaging in Cognitively Healthy, MCI, and AD Subjects
ClinicalTrials.gov study NCT02016560. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.