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78 results for “SOURCE IMAGING”
MultiCaRe: An open-source clinical case dataset for medical image classification and multimodal AI applications
<p>The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.</p> <p>More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.</p> <p>Refer to the examples showcased in this <a href="https://github.com/mauro-nievoff/MultiCaRe_Dataset">GitHub repository</a> to understand how to optimize the use of this dataset.<br><br>The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.</p>
Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism
<p>Datasets related to the research article 'Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism'. <br> A readme with the necessary Matlab code to load the data is included.<br> Tested on Matlab2020b</p> <p>For the analytic rupture radiation simulation code please check the linked github repository.</p>
Dataset - DeepWealth: A Generalizable Open-Source Deep Learning Framework using Satellite Images for Well-Being Estimation
<p>This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations.</p> <p>The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability.</p> <ul> <li>The source code can be found in <a href="https://github.com/PARSECworld/DeepWealth" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth</a></li> <li>The metadata from source code can be found in <a href="https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf</a></li> <li>The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. <em>SoftwareX</em>, 27, 101785. <a href="https://doi.org/10.1016/j.softx.2024.101785">https://doi.org/10.1016/j.softx.2024.101785</a> </li> </ul>
LASSO coherent seismic wavefield reconstruction and source imaging
<p>Coherent wavefield reconstruction and source imaging has been performed for 4 cataloged seismic events recorded with the Large-N Seismic Survey in Oklahoma (LASSO). The array consists of almost 2,000 densely spaced seismic stations and the corresponding raw time sries data have been made freely accessible by the Incorporated Research Institutions for Seismology (IRIS). The results for the 4 seismic events are accompanied with results gained for controlled seismic simulations for two of these events Reconstruction results and source images are provided in HDF5 and MAT file formats, respectively. File names were giving according to the following pattern: <br> <br> "LASSO_<<em>event name>_<reconstruction mode>_<result type>"</em></p> <p>where <<em>reconstruction mode</em>> refers either to "enhancement" (reconstruction performed for the original station layout) or "regularization" (reconstruction perfomed for a new, sense and regular station layout). <<em>result type</em>> denotes either reconstructed waveforms ("wavefield"), waveform coherence ("coherence"), or spatial source images. For the HDF5 files, mportant meta information like spatial coordinates and temporal sampling parameters are stored in a symbolic dictionary named "META", whereas the time series data is saved as a 2D matrix. Important META fields include "ntrac" (number of traces), "nt" (number of time samples), "dt" (dampling interval), "gx" (stations x coordinates), "gy" (stations y coordinates).<br> <br> The MAT files (result type "images") contain raw waveform and STA/LTA images, which are stored as 3D regular arrays named "recm1z_Enh_5_raw" (enhancement) / "recm1z_Reg5_5_raw" (regularization) and "recm1z_Enh_5_slta" (enhancement) / "recm1z_Reg5_5_slta" (regularization), respectively. For comparison, source images generated for the raw field data (without reconstruction are included in every MAT file and can be accessed through fields "recm1z_Raw_raw" and "recm1z_Raw_slta".</p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Supplementary Materials for "Simultaneous single-shot radiographic imaging using a laser-driven x-ray and proton micro-source"
<p>Simulation Data Repository, please read the contained README file in the contained simulation/ directory.</p> <p>This directory contains a copy of the used PIConGPU source code, version 0.5.0-dev-60ad9eb85 and analysis scripts.</p> <p>The PIConGPU source code is archived including its complete git history (git version 2.17.1) in source/picongpu.tar.gz with the input parameter template inside in share/picongpu/examples/Wneedle .</p> <p>Generally, PIConGPU source code is available via <a href="https://doi.org/10.5281/zenodo.591746">https://doi.org/10.5281/zenodo.591746</a> with its public git repository being maintained on <a href="https://github.com/ComputationalRadiationPhysics/picongpu">https://github.com/ComputationalRadiationPhysics/picongpu</a> .</p> <p>The two simulations’ exact input is modified accordingly in the directory input/ inside: 2D_a0-45_Z-10_ppc-20_002_light.tar.gz (p-polarized; along X) 2D_a0-45_Z-10_ppc-20_003_light.tar.gz (s-polarized; along Z).</p> <p>“Heavy” simulation data (checkpoints in simOutput/checkpoints/, full-resolution field and particle output in simOutput/bp/ ) has been stripped from this archive and are archived on NERSC’s HPSS tape archive.</p> <p>Analysis scripts are provided as Jupyter notebooks (DensityPlot_polX.ipynb and DensityPlot_polZ.ipynb) and depend on the following software:</p> <p>- adios 1.13.1 python bindings with enabled c-blosc transformations<br> - numpy 1.17.1<br> - matplotlib 3.1.1<br> - PIConGPU post-processing helper modules located in each simulation root directory under “input/lib/python/”<br> <br> The detailed conda environment can be found in the README.</p>
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
<p>Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO<sub>2</sub> incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system 'ThermoCyte', which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature 'set point' for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile.
A small body open-source dataset for image processing algorithms
<p>Crater-analog dataset acquired with a drone setup at the RIC-DFKI center. The dataset can be used to bridge the domain gap for image processing applications for lunar and small-body missions. </p>
Exploiting the potential of neutron imaging measurements for life sciences applications at the neutron spallation source, ISIS, UK - Crabs X-ray CT
<p>A ZIP file containing slices (in TIFF format) from a X-ray CT scan of crabs from the Eocene of Spain. Specimen was scanned on a Nikon Metrology HMX ST 225 system at the Natural History Museum, London, using a 1.0 mm thick copper filter, 225 kV voltage and 180 μA current, giving a tomographic dataset with a voxel size of 46 µm.</p>
Images and supporting data for high-resolution μCT of a mouse embryo using a compact laser-driven x-ray betatron source
<p>A high resolution x-ray CT scan of an embryonic mouse sample was performed with the betatron x-ray source produced by a laser wakefield accelerator. This data deposition includes all of the raw images of the mouse sample, information regarding their indexing, featured slices of the tomogram and some further raw data regarding the x-ray source characterisation.</p>
CaImAn: An open source tool for scalable Calcium Imaging data Analysis
<p>Advances in fluorescence microscopy enable monitoring larger brain areas <em>in-vivo</em> with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to preprocessing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data.</p> <p>To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets, that are contained in this open access repository. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.</p> <p>In order to reproduce the results of the paper or download the annotations and the raw movies, please refer to the readme.md at:</p> <p>https://github.com/flatironinstitute/CaImAn/blob/master/use_cases/eLife_scripts/README.md</p> <p> </p>
Object-based image analysis for monitoring plant invasions, can we use an open-source solution?
<p><strong>Introduction</strong></p> <p>This is a practical exercise testing possibilities of open-source solutions (FOSS) for object-based image analysis (OBIA) to monitor plant invasion using unoccupied aerial system (UAS, drone).</p> <p>The material is accompanying a chapter <strong><em>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier.</em></strong></p> <p>The material is meant for readers to run the workflow and detect invasion of giant hogweed on the UAS data themselves testing different FOSS solutions.</p> <p> </p> <p><strong>Data</strong></p> <p>• a subset of UAS-borne data (consumer camera) collected in Czech Republic during the flowering of a noxious invasive plant species giant hogweed (<em>Heracleum mantegazzianum</em>)</p> <p>• training dataset</p> <p>• eCognition rulebase (proprietary OBIA software)</p> <p>• a script for SegOptim package implemented in R</p> <p> </p> <p><strong>Description</strong></p> <p>The use case represents a simple application of OBIA approach based on the SegOptim package implemented in R.</p> <p>Four bands (RGBN) UAS image subset are available, capturing the central area of a heavily invaded location (CZ) by giant hogweed (Heracleum mantegazzianum). Thanks to the proper image timing, the invasive species is clearly observable as white objects (in RGB) representing the various stage of the blossom. Considering the complex shape of the flower heads, detection based on image segmentation outperforms pixel-based classification (Müllerová et al., 2017). Simple segmentation of input imagery is performed (for simplicity only the spectral bands are considered both for segmentation and feature space definition, however additional features such as vegetation indices or textural measures may be included), followed by supervised classification using training data. Finally, a visual comparison of result detection both from proprietary (eCognition) and open-source (SegOptim) implementation is provided, confirming comparable results.</p> <p>Based on #github("joaofgoncalves/SegOptim")</p> <p> </p> <p><strong>References</strong></p> <p>Gonçalves, J., Pôças, I., Marcos, B., Mücher, C. A., & Honrado, J. P. (2019). SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data. <em>International Journal of Applied Earth Observation and Geoinformation</em>, <em>76</em>, 218-230.</p> <p>Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M. & Pyšek, P. (2017b). Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring. Frontiers in Plant Science 8:1–13.</p> <p>Accompanying material for</p> <p>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier</p>
Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia
<p><strong>Dataset for GIRFReco.jl Paper</strong><br> <br> Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
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Source data: FtsK is critical for the assembly of the unique divisome complex of the FtsZ-less Chlamydia trachomatis IF images
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Data supplement to: Quality control of image sensors using gaseous tritium light sources
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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.