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469 results for “Image analysis”
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </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>
Docker Images Analysis
<p>This replication package is for the Docker container analysis submitted and accepted in SANER2019.</p> <p>Paper title: On The Relation Between Outdated Docker Containers, Severity Vulnerabilities and Bugs</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.<br> They can be automatically installed using ``pip install -r requirements.txt``. <br> The replication package also needs "docker" and "skopeo" (https://github.com/containers/skopeo) tools be installed. However, one can directly use the prepared data and analyze it, without the need to docker or skopeo.<br> These experiments were executed on a Linux Ubuntu OS.</p> <p>Content:<br> + notebooks: contains the notebook with the results and analysis.<br> + data: contains prepared data for the analysis<br> + scripts: contains other scripts needed for inspecting the images before analysis and extracting Debian packages from the Debian snapshot.</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>For the Data Extraction part, we relied on the tool of ConPan: https://github.com/neglectos/ConPan</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license.<br> The source code is under the GNU General Public License.</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>
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
Phantom imaging data and analysis macros for the article "Monochromatic computed tomography using laboratory-scale setup"
<p>The raw and processed data and analysis macros of the article <em>A.-P.</em> <em>Honkanen et S. J. Huotari, Monochromatic computed tomography using laboratory-scale setup, Scientific Reports (2023), doi:<a href="http://doi.org/10.1038/s41598-023-27409-6">10.1038/s41598-023-27409-6</a></em></p> <p>The data set consists of the raw and reconstructed computed tomography projection data taken of an PMMA phantom embedded with three different chemical species of selenium taken with a monochromatic X-ray imaging setup based on a laboratory-scale Johann-type crystal X-ray spectrometer. In addition to the imaging data, the set contains also the Jupyter Notebooks used to process and analyse the data. The details of the instrument and the analysis are presented in the article.</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International License <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Dataset related to article "Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice".
<p><strong>Datasets:</strong></p> <p><strong>Table 1_Systemic parameters.xlsx </strong>- dataset related to the systemic parameters. These data are presented in Table 1. </p> <p><strong>Figure 6_Morphometric analysis.xlsx </strong>- dataset related to the morphometric characterization of the subpodocyte space and podocytes. These data are presented in Figure 6. </p> <p><strong>Abstract of the manuscript</strong></p> <p>Transmission electron microscopy (TEM) remains the gold standard for renal histopathological diagnoses, given its higher resolving power compared to light microscopy. However, it imposes several limitations on pathologists, including longer sample preparation time and a small observation area. To overcome these, we introduced a scanning electron microscopy (SEM) technique for imaging resin-embedded semi-thin sections of renal tissue. We developed a rapid tissue preparation protocol for experimental models and human biopsies which, alongside SEM digital imaging acquisition of secondary electrons (SE-SEM), enables fast electron microscopy examination, with a resolution similar to that achieved by TEM. We used this unconventional SEM imaging approach to investigate the subpodocyte space (SPS) in BTBR <em>ob/ob</em> mice with type 2 diabetes. Analysis of semi-thin sections with secondary electrons revealed that the SPS had expanded in volume and covered large areas of the glomerular basement membrane, forming wide spaces between the podocyte body and underlying filtering membrane. Our results show that SE-SEM is a valuable tool for imaging the kidney at the ultrastructural level, filling the magnification gap between light microscopy and TEM, and reveal that in diabetic mice the SPS is larger than in normal controls, which is associated with podocyte damage and impaired kidney function.</p>
FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis calcium dataset
<p>The dataset was used in paper FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis named as 1MP. The dataset was only used to test FIOLA motion correction performance.<br> The dataset was collected for the paper Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice (link: https://pubmed.ncbi.nlm.nih.gov/24582958/) but never published before. Data was recorded in the left lobule of the cerebellum of an awake mouse using the calcium indicator GCaMP6f. GCaMP6f was selectively expressed in Purkinje cells via a combinatorial virus strategy (as explained in the paper).</p> <p>For other datasets used in paper FIOLA, check the original paper and sources they were published.</p> <p> </p> <p> </p>
Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'
<p>Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'</p>
Fig. 19. SEM and microscope images. A in A reassessment of the Neotropical genus Pseudonannolene Silvestri, 1895: cladistic analysis, biogeography, and taxonomic review (Spirostreptida: Pseudonannolenidae)
Fig. 19. SEM and microscope images. A. Head of Pseudonannolene robsoni Iniesta & Ferreira, 2014 (IBSP 3526), in frontal view. B. Head of P. occidentalis Schubart, 1958 (IBSP 1998), in frontal view. C. Collum of P. robsoni (IBSP 3506), in lateral view. D. Head of P. robsoni (IBSP 3526), in lateral view. E. Gnathochilarium of P. microzoporus Mauriès, 1987 (IBSP 3497). F. Detail of gnathochilarium of P. microzoporus (IBSP 3497). Scale bars: A–B = 500 µm; C, F, = 200 µm; D = 1 mm; E = 100 µm.
Image dataset for cow identification, including code to train deep learning model, as well as analysis of results (SmARtview, 51088)
<p>This dataset and code was a result of the UKRI project "SmARtview: An AI-powered Augmented Reality Tool for Animal Health and Productivity", linked here: <a href="https://gtr.ukri.org/projects?ref=51088">https://gtr.ukri.org/projects?ref=51088</a></p> <p>These files are intended to be used for an accompanying publication in an academic journal.</p> <p>Anyone is free to use the contents for research and teaching purposes.</p>
Analysis dataset from: Simultaneous dual-color calcium imaging in freely-behaving mice
Open the record for dataset details and reuse information.
Use of Deep Learning for structural analysis of CT-images of soil samples
Open the record for dataset details and reuse information.
BIMAGES: Bivalve images for morphological analysis and genetic estimation study
Open the record for dataset details and reuse information.
Questionnaire data, butterfly images and analysis code
Open the record for dataset details and reuse information.
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101
<p>Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies.</p> <p>Operational conditions of the analysis equipment were the following:</p> <p>- The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater.</p> <p>- The EDS analysis were collected as average on 5 points per each SEM picture</p> <p>- A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied</p>
Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
<p>This data set is designed for cell contour and cell track analysis. Hence image sequences of moving Dictyostelium discoideum cells are recorded. For the purpose to facilitate the detection of the cell contour we take fluorescent images of the cortical protein actin to obtain a high contrast between background and cell body. In each image sequences several cells are recorded. This allows for an analysis of all cells at once or to crop single cell tracks.</p>
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
<p>This data repository for MedMNIST v1 is out of date! Please check the <a href="http://medmnist.github.io">latest version</a> of MedMNIST v2. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at <a href="https://medmnist.github.io/">https://medmnist.github.io/</a>.</p> <p> </p> <p>Please note that this dataset is <strong>NOT</strong> intended for clinical use.</p> <p> </p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>pip install medmnist</pre> </blockquote> <p> </p> <p><strong>Citation and Licenses</strong></p> <p>If you find this project useful, please cite our ISBI'21 paper as:<br> <em> Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.</em><br> <br> or using bibtex:<br> <em> @article{medmnist,<br> title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},<br> author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},<br> journal={arXiv preprint arXiv:2010.14925},<br> year={2020}<br> }</em></p> <p>Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the <strong>same license</strong> as that of the source dataset.</p> <p> </p> <p><strong>PathMNIST</strong></p> <p>Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>ChestMNIST</strong></p> <p>Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a></em></p> <p> </p> <p><strong>DermaMNIST</strong></p> <p>Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.</p> <p>Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></em></p> <p> </p> <p><strong>OCTMNIST/PneumoniaMNIST</strong></p> <p>Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>RetinaMNIST</strong></p> <p>DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>BreastMNIST</strong></p> <p>Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>OrganMNIST_{Axial,Coronal,Sagittal}</strong></p> <p>Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.</p> <p>Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <div> <div class="gtx-trans-icon"> </div> </div>
Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice_image analysis
<p>This dataset contains the HE staining/anti-CD31 staining/ICG fluorescence images and the image quantification data produced for publication "Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice"</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.