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979 results for “image dataset”
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Jacobaea vulgaris and meadow image classification dataset (binary)
<h3>General Information</h3> <p>Instances in the Jacobaea vulgaris class: 895<br>Instances in the Meadow class: 9141<br>Image sizes from 77x77 to 817x817 pixels on three color channels (RGB)</p> <p> </p> <h3>Data Generation and Source</h3> <p>The images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg. The multicopter flew in a height of circa 11 meters and took pictures with a ground resolution of approximately 3,18 mm/pixel. Additional information about the process of image generation for this dataset are to be found in the relevant papers written by P. Zacharias: 1) <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf" target="_blank" rel="noopener">UAV-basiertes Grünland-Monitoring und Schadpflanzenkartierung mit offenen Geodaten</a> [p. 45–53] and 2) <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf" target="_blank" rel="noopener">UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene</a>.</p> <p>Additionally, to the images of Jacobaea vulgaris taken by the UAV, the dataset includes images of Jacobaea vulgaris plants from the internet (included in the total 895 images; e.g. images 'jkk0523.jpg', 'jkk0527.jpg'). Furthermore, some of the images of the Jacobaea vulgaris plants have been rotated, further cropped or a filter has been applied. The exact number of augmentations made is unknown. As there are augmented images included in the datasets -which makes the dataset useful for training and validation- a use of the dataset for testing purposes is not recommended due to the risk of data leakage.</p> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p> <p> </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>
Jacobaea vulgaris and meadow Augmented image classification dataset (binary)
<h3>General Information</h3> <p>Total instances: 117008<br>Instances in the Jacobaea vulgaris class: 58504 <br>Instances in the Meadow class: 58504<br>Image sizes from 224x224 pixels on three color channels (RGB)</p> <p><br>Performance increase training a ResNet50 on the base dataset versus the same architecture on the augmented data set shared here: +3,79 percent points in ROC AUC on an independent test set with 240 instances.<br><br></p> <h3>Data Generation and Source</h3> <p>The initial images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg.</p> <p>In my master's thesis at <a href="https://www.tu.berlin/dams">DAMS Lab</a> at TU Berlin, I evaluated the effect of different augmentation strategies for Jacobaea vulgaris image classification on the several performance metrics (most importantly the ROC AUC score). The identified augmentation strategies are -besides to performance based selection- also selected based on domain knowledge, which I acquired during the research for my master thesis. </p> <p>Additional information about the initial image generation process is to be found <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf">here </a> [p. 45–53] and <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf">here</a>. </p> <h3> </h3> <h3>Augmentations applied</h3> <ul> <li>Gaussian Noise: For the Gaussian noise augmentation, the mean of the added noise is set to zero. The lower and upper bounds for the random variance of the noise are 20.4663 and 54.0395 respectively. The bounds were identified by hyperparameter tuning. The search space for the lower bound was set from 5 to 30 and for the upper bound from 31 to 100. Those two search spaces were defined by visual inspection of the effects of applying Gaussian noise with different variance values to images of both classes. The Gaussian noise is sampled for each color channel individually. </li> <li>Random Brightness and Contrast: The brightness will randomly be increased or decreased by a factor ranging from 0.7010 to 1.2990. The The contrast will also be randomly increased by a factor ranging from 0.5775 to 1.4225. Those two ranges were identified using hyperparameter tuning. The search space for the maximal percentual increase or decrease of brightness and contrast was individually set from 1% to maximally 50% increase or decrease.</li> <li>Cutout Dropout: In this augmentation method a certain percentage of the input image is getting covered by black patches. The patches have a certain size in pixels, the implementation of this technique in this thesis uses square patches. The black patches are then randomly introduced into the image, by randomly alloacting the<br>patches across the image and then setting the corresponding pixel values to zero. The iamge is getting covered with patches until the cover percentage is reached. We<br>set percentage of the image to be randomly covered by black patches to 56.76%. The size of the patches, which randomly cover the image, is set to 4 pixels. A<br>good illustration of this is found in figure 4.2. The augmentation technique is inspired by the research proposed by Devries et al.[8]. Both values were identified by hyperparameter tuning. The search space for the patch size in pixels is categorical and includes the values [1, 2, 4, 7, 8, 14, 16, 28]. Those values all are multiples of 224, which is the image width and height in pixels. The patch size needs to be a multiple of the width and height in order to be suitable for the algorithm implementation. The search space for the cover percentage of the image had been set from 1% to 60%. This search space limits narrows the search down to a space where still a big part of the image is uncovered. The algorithm rearranges the image into a two dimensional grid and randomly masks rows of this grid by setting the pixel values in this row to zero. Then, the image gets rearranged, now with the randomly generated patches included.</li> <li>Random Saturation: The saturation of each pixel is randomly getting shifted. The upper bound for randomly shifting<span> </span>the saturation value of each pixel is set to 231.689%. This value was identified using hyperparameter tuning. An upper limit of the maximal saturation shift had been set to 40% shift in either direction for hyperparameter tuning.</li> <li>Horizontal Flip: The image gets flipped along the horizontal axis. </li> <li>Vertical Flip: The image gets flipped along the vertical axis.</li> <li>Random Rotation 90 degrees: Randomly rotates the image by a k-fold of 90 degrees, whereby k = {0, 1, 2, 3}.</li> </ul> <p> </p> <p>All augmentation methods and with their tuned augmentation hyperparameters (if existent) are applied to an image from the test set in figure 4.2. With the seven identified<br>augmentation techniques a dataset of 800% the size of the original dataset is created. The Augment model is trained on exactly this dataset. Of course next to the augmented images, the dataset still includes the original, unaugmented images. TensorFlow, along with additional libraries including Optuna for hyperparameter optimization and Albumentations for image augmentation, were used in for the implementation of this project.</p> <p> </p> <h3>Rational behind the augmentations applied</h3> <ul> <li>Random Rotation, Vertical and Horizontal Flip: These three augmentation strategies were chosen to make the classifier less sensitive to the orientation of the plant. The goal is to train a model that can classify plants regardless of their orientation. In order to achieve this effectively across different orientations, vertical flips, horizontal flips, and random 90-degree rotations are chosen for evaluation.</li> <li>Random Saturation: The varying saturation of the images simulates different levels of chlorophyll in the leaves, which is responsible for the green color of the<br>leaves and the intensity of this color. The color of the plant parts (leaves, stems, and flowers) is also influenced by factors such as soil, sun, weed density and pressure, location, and water availability. Varying the saturation of the images simulates changes in these factors.</li> <li>Gaussian Noise: By adding noise, in this case Gaussian noise, different lighting conditions are simulated when capturing the images. We specifically chose Gaussian<br>noise because it is common in many real-world scenarios and is based on the Central Limit Theorem, which states that the sum of many independent random variables.<br>tends to be normally distributed. This makes Gaussian noise a logical choice for simulating real-world random noise.</li> <li>Random Brightness Contrast: The Random Brightness and Random Contrast Augmentation uses brightness to mimic varying lighting conditions and contrast to highlight differences between plants by contrasting them more strongly, thereby highlighting their edges. This approach for highlighting edges is of course much more subtle than the canny edge detection augmentation. This augmentation method combines a weak focus on edges with variations in lighting conditions in one approach. The random contrast is a much softer approach for highlighting edges of plants, compared to the Canny edge detection augmentation. The other features in the images do not get changed that much, compared to the changes from edge detection augmentation.</li> <li>Cutout Dropout: The cutout augmentation simulates random occlusion by other plants. These occlusions are common and expected. Jacobaea vulgaris plants may be partially or completely obscured by other plants during image capturing. This augmentation technique makes the models more robust to random occlusion.</li> </ul> <h3> </h3> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p>
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
<h2>Abstract</h2> <p>The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. In this paper, we conduct meticulous analyses of three popular dermatological image datasets: DermaMNIST, its source HAM10000, and Fitzpatrick17k, uncovering these data quality issues, measure the effects of these problems on the benchmark results, and propose corrections to the datasets. Besides ensuring the reproducibility of our analysis, by making our analysis pipeline and the accompanying code publicly available, we aim to encourage similar explorations and to facilitate the identification and addressing of potential data quality issues in other large datasets.</p> <h2>Citation</h2> <p>If you find this project useful or if you use our newly proposed datasets and/or our analyses, please cite our paper.</p> <blockquote> <pre>Kumar Abhishek, Aditi Jain, Ghassan Hamarneh. "Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets". arXiv preprint arXiv:2401.14497, 2024. DOI: 10.48550/ARXIV.2401.14497.</pre> </blockquote> <p>The corresponding BibTeX entry is:</p> <blockquote> <p><code>@article{abhishek2024investigating,</code><br><code> title={Investigating the Quality of {DermaMNIST} and {Fitzpatrick17k} Dermatological Image Datasets},</code><br><code> author={Abhishek, Kumar and Jain, Aditi and Hamarneh, Ghassan},</code><br><code> journal={arXiv preprint arXiv:2401.14497},</code><br><code> doi = {10.48550/ARXIV.2401.14497},</code><br><code> url = {https://arxiv.org/abs/2401.14497},</code><br><code> year={2024}</code><br><code>}</code></p> </blockquote> <h2>Project Website</h2> <p>The results of the analysis, including the visualizations, are available on the project website: <a href="https://derm.cs.sfu.ca/critique/" target="_blank" rel="noopener">https://derm.cs.sfu.ca/critique/</a>.</p> <h2>Code</h2> <p>The accompanying code for this project is hosted on GitHub at <a title="Corrected-Skin-Image-Datasets" href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets" target="_blank" rel="noopener">https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets</a>.</p> <h2>License</h2> <p>The metadata files (<code>DermaMNIST-C.csv</code>, <code>DermaMNIST-E.csv</code>, <code>Fitzpatrick17k_DiagnosisMapping.xlsx</code>,<code>Fitzpatrick17k-C.csv</code>) contained in this repository are licensed under <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">the Creative Commons Attribution 4.0 International (<strong>CC BY 4.0</strong>) License</a>.</p> <p>The NPZ files associated with DermaMNIST-C (<code>dermamnist_corrected_28.npz</code>, <code>dermamnist_corrected_224.npz</code>) and DermaMNIST-E (<code>dermamnist_extended_28.npz</code>, <code>dermamnist_extended_224.npz</code>) contained in this repository are licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener">the Creative Commons Attribution-NonCommercial 4.0 International (<strong>CC BY-NC 4.0</strong>) License</a>.</p> <p>The code hosted on <a href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets" target="_blank" rel="noopener">GitHub</a> is licensed under <a href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets/blob/main/LICENSE" target="_blank" rel="noopener">the Apache License 2.0</a>.</p>
Digital Repository of Ireland Member Digitisation Workflows for 2D Image Files: Survey Questions and Dataset
<p>The Digital Repository of Ireland (DRI) issued a survey to its membership, <strong>DRI Member Digitisation Workflows for 2D Images</strong>, which ran from December 7, 2023–January 31, 2024. The survey was conducted to improve the DRI’s understanding of the technical processes and metadata workflows that our members use to digitise and share images in the Repository, in order to better tailor our support for this work and deliver the most complete information about digital images files available to our users. </p> <p>The survey informed the actions taken in WorldFAIR Project WP13 deliverable <a href="https://doi.org/10.5281/zenodo.10850009" target="_blank" rel="noopener">13.3 Implementing and Testing the Cultural Heritage Image Sharing Recommendations: DRI Case Study Report</a>. The data will inform ongoing work at DRI aimed at improving the transparency of technical information associated with digital assets accessed through the Repository.</p> <p>Read more about the Cultural Heritage Image Sharing Case Study DRI on our website: <a href="https://dri.ie/the-worldfair-project/">https://dri.ie/the-worldfair-project/</a>. </p> <p>Summary: DRI is Ireland's national repository for the arts, humanities, and social sciences data, and operates on a membership scheme. There were 20 respondents to the survey, giving us a response rate of about 35% of DRI's membership. Representation from professional fields of work across the cultural heritage sector was captured in the results (note that some institutions gave multiple responses): 17 Archives, 12 Libraries, 5 Museums and 11 Higher Education Institutions. </p>
Dataset of B-mode fatty liver ultrasound images
<p>The dataset used and described in: M. Byra, G. Styczynski, C. Szmigielski, P. Kalinowski. Ł. Michałowski4. R. Paluszkiewicz. B. Ziarkiewicz-Wróblewska, K. Zieniewicz. P. Sobieraj, A. Nowicki. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International Journal of Computer Assisted Radiology and Surgery, 2018. DOI: 10.1007/s11548-018-1843-2. </p> <p>Please refer to the above work if you use the dataset in your research. </p> <p>Contact:<br> Michal Byra<br> Department of Ultrasound<br> Institute of Fundamental Technological Research<br> Polish Academy of Sciences, Warsaw, Poland<br> mbyra@ippt.pan.pl<br> byra.michal@gmail.com</p>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</p>
Dataset of High Resolution Mammographic Images
<p>This dataset contains 138 high resolution mamographic images. Contrast Limited Adjustment Histogram Equalization (CLAHE) was used to enhance selected raw mamographic images.available in mammographic image analysis society (MIAS) database. </p>
Data for Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets
<p>Data used in MRI experiments in paper 'Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets'. For each volunteer, breath-hold data (folder bhs) and dynamic free-breathing (folder dyn) data is provided in NIFTI format.</p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
RefleX: X-ray diffraction images dataset
<p>Image dataset prepared for the RefleX study, described in <em>"Detecting anomalies in X-ray diffraction images using Convolutional Neural Networks"</em><em>.</em> The dataset contains 6311 X-ray diffraction images in 1024x1024 png format (reflex_img_1024_inter_nearest.zip). The repository also contains a file mapping each image to a set of labels (labels.csv) and files describing the assignment of each image to training, validation, and testing sets (labels_train.csv, labels_val.csv, labels_test.csv).</p> <p>The dataset can be used for multi-label classification. Each diffraction image can exhibit any combination of seven classes: Ice ring, Diffuse Scattering, Background Ring, Non-uniform Detector, Loop Scattering, Strong Background, and Artifact.</p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Nanolaminography dataset: Three-dimensional imaging of integrated circuits with a macro to nanoscale zoom
<p>Here we present a ptychographic X-ray laminography (PyXL) dataset. It is a new approach for nano-imaging that combines a coherent diffractive imaging technique called ptychography with laminography, which is a generalization of tomography. This allows achieving sub-20 nm resolution over large sample volumes. </p> <p>Non-destructive three-dimensional imaging over large volumes with nano-scale resolution is a challenge faced in many fields, perhaps most acutely in mapping the natural neural connectome and artificial silicon-based integrated circuits. For the latter, such inspection is of interest for quality control and security acquiring particular importance due to the delocalized nature of the chain connecting chip design, manufacture and use. A hierarchy of probes are used to image at length scales from that of the entire chip (millimeters) to those of individual features (nanometers) of the underlying transistors, starting with optical microscopy and finishing with transmission electron microscopy on thin slices prepared using a focused ion beam. What has been missing until now is a single technique yielding a three-dimensional image of the entire chip volume with zooming capability to produce high-resolution images of arbitrarily chosen sub-regions, including virtual delayering.</p> <p>The related publication can be accessed via ShareIt <a href="https://mail.ethz.ch/owa/redir.aspx?C=NzB9-v6wS5uDOPXWL0cW_Vu4ys_MIpbvUqKcmezLt5AHOlhZD1DXCA..&URL=https%3a%2f%2frdcu.be%2fbTudW">https://rdcu.be/bTudW</a></p>
Representative Sample Dataset for Resolution-Agnostic Tissue Segmentation in Whole-Slide Histopathology Images
<p>This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.</p> <p>The dataset is composed of two parts: <strong>development set</strong> and <strong>dissimilar set</strong>.</p> <p>Sample images from the development set:</p> <ul> <li>breast_hne_00.tif</li> <li>breast_lymph_node_hne_00.tif</li> <li>tongue_ae1ae3_00.tif</li> <li>tongue_hne_00.tif</li> <li>tongue_ki67_00.tif</li> </ul> <p>Sample images from the dissimilar set:</p> <ul> <li>brain_alcianblue_00.tif</li> <li>cornea_grocott_00.tif</li> <li>kidney_cab_00.tif</li> <li>skin_perls_00.tif</li> <li>uterus_vonkossa_00.tif</li> </ul>
Macroscopic, histological and stereological image dataset of Megrim (Lepidorhombus whiffiagonis) ovaries from the ICES Celtic Seas, south of Greater North sea or Bay of Biscay Ecoregions
<p><strong>Contents: </strong></p> <p>This dataset contains the macroscopic and histological images of the ovaries of 202 Megrim (female, <em>Lepidorhombus whiffiagonis</em>, Walbaum, 1792) collected from the ICES Celtic Seas, south Greater North sea or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7h & 7j), November 2020 (n=14, Eco=7h & 7j), December 2020 (n=1, Eco=7h), May 2021 (n=15, Eco=7h & 7g), June 2021 (n=15, Eco=7h), July 2021 (n=15, Eco=7h & 7e), October 2021 (n=15, Eco=7g & 7f), October 2021 (n=6, Eco=8a & 8b & 8c), November 2021 (n=6, Eco=8a & 8b), November 2021 (n=15, Eco=7j), December 2021 (n=15, Eco=7e & 7g), January 2022 (n=15, Eco=7f), February 2022 (n=15, Eco=7g), March 2022 (n=15, Eco=7g) and May 2022 (n=15, Eco=7g).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 549 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 202 female megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> </li> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 461 histological slides acquired during this study. </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 3 different agents, with the first and last readings, as well as the QuPath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 102 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 102 slides belong to 17 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 202 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> <li><strong>Macro_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_WHI.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_WHI.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 202 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> <li><strong>Stereo_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_WHI.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_WHI.csv</strong> : a text data file (.csv) of the stereology count results of 287 slides read during this study. Among these slides, 102 were read to test the homogeneity distribution of different cell types found throughout each ovary (17 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 3 agents). Finally, 202 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul> <p> </p> <p><strong>Contact :</strong></p> <p>This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr</p>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample
<h3><strong>Dataset Description</strong></h3> <p>This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M03 sample). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample.</p> <p><strong>Core Location in the M03 Cube Sample:</strong></p> <ul> <li><strong>Z:</strong> 12.5 cm</li> <li><strong>South-North:</strong> 12.5 cm</li> <li><strong>West-East:</strong> 11.5 cm to 1.36 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M03 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M03 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA.</p> <p>The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M03, 6 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection.</p> <p>The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample.</p> <h4><strong>Key Features:</strong></h4> <ul> <li> <p><strong>Fracture Characteristics</strong>: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process.</p> </li> <li> <p><strong>CT-Scan Details</strong>: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D.</p> </li> <li> <p><strong>Material Information</strong>: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium.</p> </li> <li> <p><strong>Experimental Context</strong>: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media.</p> </li> </ul> <h4><strong>Applications:</strong></h4> <p>This dataset is particularly valuable for researchers and engineers involved in:</p> <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4><strong>File Structure:</strong></h4> <p>The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path.</p> <h4><strong>Processing code:</strong></h4> <p>Follow the <strong>URL repository</strong> in the software section to access to the code for processing these images and reconstructing the fracture surfaces.</p> <p><strong>Acknowledgment:</strong></p> <p>We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided.</p> <p><strong>Contact and Support:</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
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