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38,240 results for “Imaging”
Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis
<p>This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.</p> <p>The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:<br> * id: unique identifier within each partition.<br> * tilted_label: image-level label corresponding to the tilted rectangle.<br> * nontilted_label: image-level label corresponding to the non-tilted rectangle.<br> * tilted_size: longest size of the tilted rectangle.<br> * nontilted_size: longest size of the non-tilted rectangle.<br> * kind: either "tile" or "mask" image type.</p> <p>The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".</p> <p>The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.</p>
Image segmentations produced by BAMF under the AIMI Annotations initiative
<p>The Imaging Data Commons (IDC)(<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provide an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections.</p> <p>To validate the model's performance, roughly 10% of the AI predictions were assigned to a validation set. For this set, a board-certified radiologist graded the quality of AI predictions on a Likert scale. If they did not 'strongly agree' with the AI output, the reviewer corrected the segmentation. </p> <p>This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images.</p> <p><em>Only 10% of the AI-derived annotations provided in this dataset are verified by expert radiologists . More details, on model training and annotations are provided within the associated manuscript to ensure transparency and reproducibility.</em></p> <p> </p> <p>This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 1.x collections, a medical student (non-expert) reviewed all the AI predictions and rated them on a 5-point Likert Scale, for any AI predictions in the validation set that they did not 'strongly agree' with, the non-expert provided corrected segmentations. This non-expert was not utilized for the Version 2.x additional records.</p> <p> </p> <h3>Likert Score Definition:</h3> <p>Guidelines for reviewers to grade the quality of AI segmentations.</p> <ul> <li>5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change)</li> <li>4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable</li> <li>3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome</li> <li>2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch</li> <li>1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable.</li> </ul> <p> </p> <h3>Zip File Folder Structure</h3> <p>Each zip file in the collection correlates to a specific segmentation task. The common folder structure is</p> <ul> <li><em>ai-segmentations-dcm </em>This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files</li> <li><em>qa-segmentations-dcm </em>This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*)</li> <li><em>qa-results.csv</em> CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance.</li> </ul> <p> </p> <h3><strong><em>qa-results.csv Columns</em></strong></h3> <p>The qa-results.csv file contains metadata about the segmentations, their related IDC case image, as well as the Likert ratings and comments by the reviewers.</p> <div> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p><em>Collection</em></p> </td> <td> <p>The name of the IDC collection for this case</p> </td> </tr> <tr> <td> <p><em>PatientID</em></p> </td> <td> <p>PatientID in DICOM metadata of scan. Also called Case ID in the IDC</p> </td> </tr> <tr> <td> <p><em>StudyInstanceUID</em></p> </td> <td> <p>StudyInstanceUID in the DICOM metadata of the scan</p> </td> </tr> <tr> <td> <p><em>SeriesInstanceUID</em></p> </td> <td> <p>SeriesInstanceUID in the DICOM metadata of the scan</p> </td> </tr> <tr> <td> <p><em>Validation</em></p> </td> <td> <p>true/false if this scan was manually reviewed</p> </td> </tr> <tr> <td> <p><em>Reviewer</em></p> </td> <td> <p>Coded ID of the reviewer. Radiologist IDs start with ‘rad’ non-expect IDs start with ‘ne’</p> </td> </tr> <tr> <td> <p><em>AimiProjectYear</em></p> </td> <td> <p>2023 or 2024, This work was split over two years. The main methodology difference between the two is that in 2023, a non-expert also reviewed the AI output, but a non-expert was not utilized in 2024.</p> </td> </tr> <tr> <td> <p><em>AISegmentation</em></p> </td> <td> <p>The filename of the AI prediction file in DICOM-seg format. This file is in the ai-segmentations-dcm folder.</p> </td> </tr> <tr> <td> <p><em>CorrectedSegmentation</em></p> </td> <td> <p>The filename of the reviewer-corrected prediction file in DICOM-seg format. This file is in the qa-segmentations-dcm folder. If the reviewer strongly agreed with the AI for all segments, they did not provide any correction file.</p> </td> </tr> <tr> <td> <p><em>Was the AI predicted ROIs accurate?</em></p> </td> <td> <p>This column appears one for each segment in the task for images from AimiProjectYear 2023. The reviewer rates segmentation quality on a Likert scale. In tasks that have multiple labels in the output, there is only one rating to cover them all.</p> </td> </tr> <tr> <td> <p><em>Was the AI predicted {SEGMENT_NAME} label accurate?</em></p> <p><em><strong> </strong></em></p> </td> <td> <p>This column appears one for each segment in the task for images from AimiProjectYear 2024. The reviewer rates each segment for its quality on a Likert scale.</p> </td> </tr> <tr> <td> <p><em>Do you have any comments about the AI predicted ROIs?</em></p> <p><em><strong> </strong></em></p> </td> <td> <p>Open ended question for the reviewer</p> </td> </tr> <tr> <td> <p><em>Do you have any comments about the findings from the study scans?</em></p> </td> <td> <p>Open ended question for the reviewer</p> </td> </tr> </tbody> </table> </div> <p> </p> <h3>File Overview</h3> <h4>brain-mr.zip</h4> <ul> <li>Segment Description: brain tumor regions: necrosis, edema, enhancing</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/upenn-gbm/">UPENN-GBM</a></li> <li>Links: <a href="../records/11582627">model weights</a>, <a href="https://github.com/bamf-health/aimi-brain-mr">github</a></li> </ul> <h4>breast-fdg-pet-ct.zip</h4> <ul> <li>Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/qin-breast/">QIN-Breast</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290054">model weights, </a><a href="https://github.com/bamf-health/aimi-breast-pet-ct">github</a></li> </ul> <h4>breast-mr.zip</h4> <ul> <li>Segment Description: Breast, Fibroglandular tissue, structural tumor</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/duke-breast-cancer-mri/">duke-breast-cancer-mri</a></li> <li>Links: <a href="../records/11998679">model weights</a>, <a href="https://github.com/bamf-health/aimi-breast-mr">github</a></li> </ul> <h4>kidney-ct.zip</h4> <ul> <li>Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans</li> <li>IDS Collection: <a href="https://www.cancerimagingarchive.net/collection/tcga-kirc/">TCGA-KIRC,</a> <a href="https://www.cancerimagingarchive.net/collection/tcga-kirp/">TCGA-KIRP</a>, <a href="https://www.cancerimagingarchive.net/collection/tcga-kich/">TCGA-KICH</a>, <a href="https://www.cancerimagingarchive.net/collection/cptac-ccrcc/">CPTAC-CCRCC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8277845">model weights, </a><a href="https://github.com/bamf-health/aimi-kidney-ct">github</a></li> </ul> <h4>liver-ct.zip</h4> <ul> <li>Segment Description: Liver from CT scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/TCGA-LIHC/">TCGA-LIHC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8270230">model weights, </a><a href="https://github.com/bamf-health/aimi-liver-ct">github</a></li> </ul> <h4>liver2-ct.zip</h4> <ul> <li>Segment Description: Liver and Lesions from CT scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/hcc-tace-seg/">HCC-TACE-SEG</a>, <a href="https://www.cancerimagingarchive.net/collection/colorectal-liver-metastases/">COLORECTAL-LIVER-METASTASES</a></li> <li>Links: <a href="../records/11582728">model weights</a>, <a href="https://github.com/bamf-health/aimi-liver-tumor-ct">github</a></li> </ul> <h4>liver-mr.zip</h4> <ul> <li>Segment Description: Liver from T1 MRI scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/TCGA-LIHC/">TCGA-LIHC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290123">model weights, </a><a href="https://github.com/bamf-health/aimi-liver-mr">github</a></li> </ul> <h4>lung-ct.zip</h4> <ul> <li>Segment Description: Lung and Nodules (3mm-30mm) from CT scans</li> <li>IDC Collections:<br> <ul> <li><a href="https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/">Anti-PD-1-Lung</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/">LUNG-PET-CT-Dx</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/">NSCLC Radiogenomics</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/rider-lung-pet-ct/">RIDER Lung PET-CT</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUAD/">TCGA-LUAD</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUSC/">TCGA-LUSC</a></li> </ul> </li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290146">model weights 1, </a><a href="../record/8290169">model weights 2, </a><a href="https://github.com/bamf-health/aimi-lung-ct">github</a></li> </ul> <h4>lung2-ct.zip</h4> <ul> <li>Improved model version</li> <li>Segment Description: Lung and Nodules (3mm-30mm) from CT scans</li> <li>IDC Collections:<br> <ul> <li><a href="https://www.cancerimagingarchive.net/collection/QIN-LUNG-CT">QIN-LUNG-CT</a>, <a href="https://www.cancerimagingarchive.net/collection/spie-aapm-lung-ct-challenge/">SPIE-AAPM Lung CT Challenge</a></li> </ul> </li> <li>Links: <a href="../records/11582738">model weights</a>, <a href="https://github.com/bamf-health/aimi-lung2-ct">github</a></li> </ul> <h4>lung-fdg-pet-ct.zip</h4> <ul> <li>Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans</li> <li>IDC Collections: <ul> <li><a href="https://www.cancerimagingarchive.net/collection/acrin-nsclc-fdg-pet/">ACRIN-NSCLC-FDG-PET</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/">Anti-PD-1-Lung</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/">LUNG-PET-CT-Dx</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/">NSCLC Radiogenomics</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/rider-lung-pet-ct/">RIDER Lung PET-CT</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUAD/">TCGA-LUAD</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUSC/">TCGA-LUSC</a></li> </ul> </li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290054">model weights, </a><a href="https://github.com/bamf-health/aimi-lung-pet-ct">github</a></li> </ul> <h4>prostate-mr.zip</h4> <ul> <li>Segment Description: Prostate from T2 MRI scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/ProstateX/">ProstateX,</a> <a href="https://www.cancerimagingarchive.net/collection/prostate-mri-us-biopsy/">Prostate-MRI-US-Biopsy</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290092">model weights, </a><a href="https://github.com/bamf-health/aimi-prostate-mr">github</a></li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>2.0.2 - Fix the brain-mr segmentations to be transformed correctly</li> <li>2.0.1 - added AIMI 2024 radiologist comments to qa-results.csv</li> <li>2.0.0 - added AIMI 2024 segmentations</li> <li>1.X - AIMI 2023 segmentations and reviewer scores</li> </ul>
Dataset of Scanning Tunneling Microscopy (STM) images of graphene on nickel
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p> </p>
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Inkjet Captured images
<p>This database consists of 322,193 snapshots capturing the output of the inkjet device under a variety of operational parameters. To facilitate access, the images are randomly divided into 7 folders, each containing over 46,000 images. The accompanying dataset, 'results.csv,' includes the extracted features from the images:</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>n</strong></td> <td>number of identified droplets</td> </tr> <tr> <td><strong>n_contours</strong></td> <td>number of identified contours</td> </tr> <tr> <td><strong>(c_x, c_y)</strong></td> <td>position of the centroid of the principal droplet</td> </tr> <tr> <td><strong>(a, b)</strong></td> <td>semi-axes of the fitted ellipse to the principal droplet</td> </tr> <tr> <td><strong>theta</strong></td> <td>the orientation of the principal droplet</td> </tr> </tbody> </table> <p>For further details, please refer to the <a href="https://droplets.cimne.com/" target="_blank" rel="noopener">DropletDynamics group webpage</a> and our <a href="https://github.com/playpaolo/InkJetDroplet" target="_blank" rel="noopener">GitHub repository</a>.</p> <p><strong>Note:</strong> This is part of a larger dataset, with the remaining portion uploaded separately due to file size limitations. Please refer to the <a href="https://doi.org/10.5281/zenodo.13889374">other associated upload</a> for the complete dataset.</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under CC BY-NC-SA for non-commercial use.<br>If you modify or adapt the dataset, you must share the modified version under the same license (CC BY-NC-SA) and credit the original source.<br>For commercial use, a separate license is required—please contact <a href="mailto:droplets@cimne.upc.edu">droplets@cimne.upc.edu</a> for details.</p>
MORSE image reconstruction example dataset
<p>This repository contains raw k-space datasets from 3T and 7T multi-echo spoiled gradient echo MRI scans, which along with the source code uploaded on <a href="https://github.com/fil-physics/gadgetron-matlab">GitHub</a>, can be used to demonstrate FIL Physics MORSE image reconstruction as in the following manuscript:</p> <p>"MORSE CODE: Multiple Orthogonal Reference Sensitivity Encoding Combined Over Dominant Eigencoils" by O. Josephs and B. Dymerska et al.</p> <p>If you use these data or MORSE image reconstruction, please make sure to cite the paper.</p>
IPTIS (individual pomelo tree image sample) datasets
<p>These datasets include 480 clip images from two study sites (i.e., Site A and B) totally. The origional UAV-based images were captured with DJI drones on four different dates, i.e., 3 Debruary 2021, 12 March 2021, 12 April 2021, and 16 January 2022. They were proceeded into four separate datasets according to the date and one in total. They were used for the study on Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 by Y Xiong, X Zeng, W Lai, J Liao, Y Chen, M Zhu, and K Huang, which was published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7554 - 7576, 2024(22 March 2024). https://doi.org/10.1109/JSTARS.2024.3379522.<br>They were named IPTIS (individual pomelo tree image sample) datasets for short.</p>
Direct imaging of carbohydrate stereochemistry structural dataset
<p>Dataset includes. </p> <ol> <li>Training data for the NequIP model (all_4NPxG_mod_E.extxyz) including structures, energies and force components.</li> <li>Zip file (4NPxG_training_run.zip) containing training parameters (config.yaml) and metrics (.csv files) and the deployed NequIP model (deployed_model.pth) used for minima hopping in the corresponding study.</li> <li>Bayesian Optimization Structure Search results for conformers (alpha/beta-4-Nitrophenyl-D-Galacturonide_opt.extxyz) and isolated adsorbates (alpha/beta_isolated_adsorbates_opt.extxyz) on Au(111). DFT relaxed structures.</li> <li>CREST NADG conformers (crest_conformers_alpha.extxyz). </li> <li>Initial monolayer structure relaxations (4NPaG/4NPbG_monolayer_relaxation_every_fifth.extxyz). Every fifth geometry from the relaxation.</li> <li>Results from NequIP minima hopping for monolayer structures as trajectory files (alpha/beta_minima_hopping_nequip.traj). Contains also protonated NADG structures (alpha_protonated_minima_hopping_nequip.traj).</li> <li>Final NADG and NBDG monolayer structures with Hartree potentials and STM images simulated with FHI-aims (Final_NADG/NBDG_hartree_potential.cube, Final_NADG/NBDG_stm_01.cube) with the z-maps (Final_NADG/NBDG_stm_z_map.cube) for creating STM image contrast.</li> </ol>
Research Data Management and Sharing for images: beautiful fountains require ugly piping!
<p>The consensus is clear: research data funded by public resources should be shared. Globally, the advantages of sharing research data are widely recognized. It promotes transparency and validation, reduces redundant efforts, accelerates discovery, enhances equity, and increases the impact of research through collaboration and efficient use of resources.</p> <p>Image data, however, presents unique challenges. Advanced technologies produce large, multimodal, and multiplexed datasets that span multiple targets across various spatiotemporal scales.</p> <p>This image data comes from a range of sources—such as optical, electron microscopy, and medical imaging—each with specific technical requirements. Managing this complexity is a daunting task without global metadata standardization as well as robust Research Data Management and Sharing (RDMS) cyberinfrastructure to bring it all together.</p> <p>The figure illustrates a common issue: while the importance of the <strong>“beautiful fountains”</strong> of scientific discoveries and medical treatments is widely understood, fewer people recognize the <strong>need to invest in building the often ignored “ugly plumbing” </strong>required to build a strong RDMS cyberinfrastructure.</p> <p> </p>
Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging
<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue). We used *Movie2* from the Supplementary data.</div> <br> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
Testing whole slide image for OpenPhi - Open Pathology Interface
<p>An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.</p>
Plant image identification application demonstrates high accuracy in Northern Europe dataset
<p><strong>Images and data for the study "Plant image identification application demonstrates high accuracy in Northern Europe"</strong></p> <p><strong>Details: Jaak Pärtel, Meelis Pärtel, Jana Wäldchen, Plant image identification application demonstrates high accuracy in Northern Europe, <em>AoB PLANTS</em>, Volume 13, Issue 4, August 2021, plab050, <a href="https://doi.org/10.1093/aobpla/plab050">https://doi.org/10.1093/aobpla/plab050</a></strong></p> <p>The data table displays Flora Incognita's identification results together with species and observations characteristics. All (3199) used images are included.</p> <p>The study was conducted in two parts: database and field study.</p> <p>Database study images have been taken from eBiodiversity database (https://elurikkus.ee/en) under Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). Please cite the original source for the images as well when using the dataset.</p> <p>Field study images were taken by Jaak Pärtel in 2020 in field conditions from different habitats across Estonia.</p>
Infrared Chemical Image of a Breast Cancer Tissue Microarray
<p>This data set relates to an open access paper published in Analyst <em>Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets</em> by Jiayi Tang, Alex Henderson* and Peter Gardner. <a href="https://doi.org/10.1039/D0AN02155E"> https://doi.org/10.1039/D0AN02155E</a></p> <p>The files in this archive are mid-infrared spectroscopy chemical images of a breast cancer tissue microarray. The tissue microarray is BR20832 from Biomax. <a href="http://www.biomax.us/tissue-arrays/Breast/BR20832">http://www.biomax.us/tissue-arrays/Breast/BR20832</a></p> <p>Processed versions of these data in MATLAB file format can be found in another Zenodo archive at <a href="https://doi.org/10.5281/zenodo.4730312">https://doi.org/10.5281/zenodo.4730312</a></p> <p>This processed data refers to a paper published in Analyst</p>
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
<p><strong>Overview:</strong></p> <p>This repository contains the <strong>raw images</strong> for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>.</p> <p>This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p><strong>Usage:</strong></p> <p>Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:</p> <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <p><strong>File Numbering/Naming Scheme:</strong></p> <p>The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.</p> <p>To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong><a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a></strong> (see the scripts/alignment/ directory).</p> <p>For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>. The CSV format is as follows:</p> <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre> <p> </p>
Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging
<p>The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." <em>Proteomics</em> 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication.</p> <p>The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue.</p> <p>Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured.</p> <p>Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within <em>m/z</em> range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels.</p> <p>An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist.</p> <p>The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the <em>m/z</em> axis across the dataset. The baseline was removed with MATLAB procedure <em>msbackadj()</em> from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.</p>
Supporting data for manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging"
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging" accepted for publication at Geochemistry, Geophysics, Geosystems (10.1029/2022GC010595). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 29Si, 43Ca, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. In a sample of the EGRIP Greenland ice core (from about 1256.95 m depth), maps were obtained over 3 adjacent areas. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file, named as “ds01_Area1_Na.csv”, etc. These maps were obtained using a 20 µm square spot. This data can be used to obtain the images shown in the manuscript. For the additional map shown as Figure 9 in the manuscript, data were obtained using a LA-ICP-TOFMS for imaging a sample of the last glacial period in the EPICA Dome C (EDC) ice core, bag 1065. The maps were acquired using a 35 µm square spot, with 50% overlap between neighboring pixels to increase the spatial resolution horizontally.</p>
Scanning electron microscope images of spruce needle homogenate and scanning electron microscope images of isolated small cellular particles from spruce needle homogenate
<p>Scanning electron microscope images of spruce needle homogenate and of isolated small cellular particles from spruce needle homogenate are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics, and Anna Romolo, presently at University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Physics, Ljubljana, Slovenia. Present address of Marko Jeran is: Department of Inorganic Chemistry and Technology, “Jožef Stefan” Institute, Ljubljana, Slovenia.</p>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
Example imaging mass cytometry raw data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the <strong>I</strong>ntegrated i<strong>MMU</strong>noprofiling of large adaptive <strong>CAN</strong>cer patient cohorts (IMMUcan) project (<a href="https://immucan.eu">immucan.eu</a>) using the Hyperion imaging system (<a href="https://www.fluidigm.com/products-services/instruments/hyperion">www.fluidigm.com/products-services/instruments/hyperion</a>). To get an overview on the technology and available analysis strategies, please visit <a href="https://bodenmillergroup.github.io/IMCWorkflow/">bodenmillergroup.github.io/IMCWorkflow</a>. The individual data files are described below:</p><ul><li><strong>Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip</strong>: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.</li><li><strong>compensation.zip</strong>: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: <a href="https://doi.org/10.1016/j.cels.2018.02.010">Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry</a></li><li><strong>panel.csv</strong>: This file contains metadata for each antibody/channel used in the experiment. The <i>full</i> column indicates which channel should be analysed. The <i>ilastik</i> column specifies which channels were used for ilastik pixel classification and the <i>deepcell</i> column indicates the channels used for deepcell segmentation.</li><li><strong>sample_metadata.csv</strong>: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).</li></ul>
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.