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979 results for “image dataset”
Seized ecstasy pills: image dataset (full resolution jpg)
<p>According to the World Drug Report 2020, cocaine and ecstasy are the most consumed stimulant drugs, with 19 and 27 millions estimated users in 2018. Unsurprisingly, large efforts are being made to design fast and cost effective analytical methods to track and monitor the distribution networks of these synthetic drugs. Here we share two datasets of ecstasy pills seized in the north-east of Switzerland between 2010 and 2011. The first contains 621 forensic grade images of pills, while the second one consists of 486 mIR spectra. While both sets are not covering the same seizure, both provide high quality data with orthogonal information to evaluate clustering and dimension reduction methods.</p> <p> </p>
Seized ecstasy pills: infrared spectra and image datasets (low resolution jpg)
<p>According to the World Drug Report 2020, cocaine and ecstasy are the most consumed stimulant drugs, with 19 and 27 millions estimated users in 2018. Unsurprisingly, large efforts are being made to design fast and cost effective analytical methods to track and monitor the distribution networks of these synthetic drugs. Here we share two datasets of ecstasy pills seized in the north-east of Switzerland between 2010 and 2011. The first contains 621 forensic grade images of pills, while the second one consists of 486 mIR spectra. While both sets are not covering the same seizure, both provide high quality data with orthogonal information to evaluate clustering and dimension reduction methods.</p> <p>High resolution jpg files can be found here: 10.5281/zenodo.4124562</p>
Supplementary Dataset for Detecting structured sources in noisy images via Minkowski maps
<p>Supplementary Dataset of the publication "<a href="https://dx.doi.org/10.1209/0295-5075/128/60001">Detecting structured sources in noisy images via Minkowski maps</a>" by the same authors:</p> <p>M. A. Klatt, K. Mecke. <em>EPL (Europhysics Letters)</em> <strong>128</strong>:60001 (2019)<br> <a href="https://dx.doi.org/10.1209/0295-5075/128/60001">https://dx.doi.org/10.1209/0295-5075/128/60001</a></p> <p>The dataset contains all simulated data and parameters of the figures and table in the main text and supplementary material.</p> <p>The corresponding code is available at the GitHub repository:<br> <a href="https://github.com/michael-klatt/minkmaps">https://github.com/michael-klatt/minkmaps</a></p>
Supplementary data to accompany "Abundant metabolite-matrix adducts illuminate the dark metabolome of MALDI-mass-spectrometry imaging datasets"
<p>This dataset accompanies the publication "Abundant metabolite-matrix adducts illuminate the dark metabolome of MALDI-mass-spectrometry imaging datasets". The dataset includes all files, scripts and results that are included in the associated publication.</p> <p>Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds or thousands of metabolites across biological systems. One major challenge is the complexity of the data, which includes signals from experimental artifacts. Formation of adducts (<em>e.g. </em>with Na+or K+) or abundant matrix-cluster, in the case of matrix-assisted laser desorption ionization (MALDI)-MSI, strongly increase peak counts. We developed <em>mass2adduct</em>, a universally applicable tool for adduct abundance estimations in high-mass-resolution spatial metabolomics datasets. Our study illustrates that MALDI-MSI data density is remarkably driven by adduct formation and revealed a major influence of so far unrecognized metabolite-matrix adducts on total peak counts. Current data analyses neglect those matrix adducts and therefore overestimate total metabolite numbers, thereby inflating the dark metabolome size.</p> <p>mass2adduct zenodo doi (10.5281/zenodo.1405088)</p> <p>mass2adduct gihub: https://github.com/kbseah/mass2adduct</p>
PSF Estimation and deconvolution: models, microscopy images, and datasets
<p>This is the accompanying dataset for the publication Adrian Shajkofci, Michael Liebling, “Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy,” IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020.</p> <p>Publications based on this data must cite the above paper.<br> <br> BibTeX Citation:<br> @ARTICLE{shajkofci.liebling:20,<br> author={A. Shajkofci and M. Liebling},<br> journal={IEEE Trans. Image Proces.}, <br> title={Spatially-Variant {CNN}-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy},<br> year={2020},<br> volume={29},<br> number={},<br> pages={5848-5861},<br> doi={10.1109/TIP.2020.2986880}}<br> </p> <p>In the archive, you will find :</p> <ul> <li>- Trained models for PSF estimation and deconvolution</li> <li>- Synthetic training dataset of cells and beads</li> <li>- Stacks of multi-channel fluorescence microscopy images of HeLa cells, rat brain cells, beads and plant cells to test the PSF estimation tool, deconvolution algorithm or auto-focus algorithm.</li> <li>- Stacks of tilted grid (3, 6 and 9 degrees) using astigmatic lenses for depth estimation.<br> </li> </ul> <p>The code for running the models is available here:<br> <a href="https://github.com/idiap/psfestimation">https://github.com/idiap/psfestimation</a></p> <p><br> <strong>Reference paper</strong></p> <p>A. Shajkofci and M. Liebling, "Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy," in IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020, doi: 10.1109/TIP.2020.2986880.</p> <p> </p> <p><strong>Ethical compliance</strong></p> <p>The post-mortem stained and fixed tissue slices whose images are included in this data set were reused from experiments approved by the EPFL ethics committee.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Swiss National Science Foundation under Grants 206021_164022 “Platform for Reproducible Acquisition, Processing, and Sharing of Dynamic, Multi-Modal Data” and 200020_179217 “COMPBIO: Computational biomicroscopy: advanced image processing methods to quantify live biological systems”</p>
UAV thermal image dataset
<p>The thermal images dataset is captured by DJI Matrice 210 Drone equipped with FLIR thermal camera for SAR operation.</p>
Dataset Sachindra et al: SPECT/CT imaging, biodistribution and radiation dosimetry of a 177Lu-DOTA-integrin αvβ6 cystine knot peptide in a pancreatic cancer xenograft model
<p>Numerical data for the manuscript 'SPECT/CT imaging, biodistribution and radiation dosimetry of a 177Lu-DOTA-integrin αvβ6 cystine knot peptide in a pancreatic cancer xenograft model'</p>
Dataset for the publication: Multi-line SiO fluorescence imaging in the flame synthesis of silica nanoparticles from SiCl4
<p>Dataset for the publication: </p> <p>Multi-line SiO fluorescence imaging in the flame synthesis of silica nanoparticles from SiCl4</p> <p>Abbas El Moussawi, Torsten Endres, Sebastian Peukert, Siavash Zabeti, Thomas Dreier, <br> Mustapha Fikri, Christof Schulz </p> <p>in Combustion and Flame</p> <p>This dataset contains the raw LIF imaging data (LIF_Imaging_Rawdata.7z), an overview of the measured flame conditions and their parameters (MeasumentsOverview.xlsx), the determined temperature and SiO-concentration profiles (T&C-SiO-LIF.Exp.Profiles.xlsx), the Origin/Matlab figures from the publication (OriginData&Figures.7z) and the Chemkin project for the kinetic simulations (Chemkin.7z).Remarks.docx contains instructions for performing the Chemkin simulations and information about the software&version used.</p> <p><br> The Matlab code for analysing the raw data can be found in the separate Zenodo publication:<br> <a href="https://doi.org/10.5281/zenodo.4399312">10.5281/zenodo.4399312</a><br> However, this is embargoed for 12 months, as a better documented release of the code is planned during this period.</p>
Coin Image Dataset
<p><strong>Description</strong></p> <p>The coin image dataset is a dataset of 60 classes of Roman Republican coins. Each class is represented by three coin images of the reverse side acquired at Coin Cabinet of the Museum of Fine Arts in Vienna, Austria. The dataset has been used for evaluation in [1].</p> <p><strong>Usage</strong></p> <p>The dataset is freely available for non-commercial research use. Please be aware that the image rights belong to the Museum of Fine Arts in Vienna, Austria. Please also cite our paper [1] when using the dataset for your research.</p> <p><strong>Technical Details</strong></p> <p>The image filenames have the following syntax: class[classid]_image[1-3].png<br> The dataset also contains a CSV-file “classes.csv” which maps the class-IDs to the reference numbers defined by Crawford’s standard reference book [2].</p> <p>References</p> <p>[1] Zambanini S., Kampel M. “<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-37484-5_3">Coarse-to-Fine Correspondence Search for Classifying Ancient Coins</a>“, <em>2nd ACCV Workshop on e-Heritage</em>, pp. 25-36, Daejeon, South Korea, November 2012. (<a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/accv-eh-12.pdf">pdf</a>)</p> <p>[2] Crawford, M.H.: “Roman Republican Coinage”, 2 vols., Cambridge University Press, 1974.</p>
SIDIRE: Synthetic Image Dataset for Illumination Robustness Evaluation
<p>SIDIRE is a freely available image dataset which provides synthetically generated images allowing to investigate the influence of illumination changes on object appearance. The images are renderings of 3D coin models with different material BRDFs and levels of texturedness. Thus, the dataset makes it possible to directly evaluate the influence of these conditions on the performance of image recognition without introducing a bias due to different objects used between image sets. The dataset has been used for evaluation in [1].</p> <p><strong>Usage</strong></p> <p>The dataset is freely available for non-commercial research use. Please cite our paper [1] when using the dataset for your research.</p> <p><strong>Technical Details</strong></p> <p>Full Image Dataset</p> <p>The full image dataset consists of images of 14 coin models which have been rendered using the open-source graphics software <a href="http://www.blender.org">Blender</a>. For each model, twelve sets of 500×500 images with 65 illumination directions were rendered where each set represents one out of four material BRDFs and one out of three texture density levels. Material BRDFs are intended to represent different levels of specularity starting from a Lambertian material with zero specularity up to specular intensity values of 0.25, 0.50 and 1.00. The first texture density level shows no texture and thus represents the set of textureless objects. For the remaining two levels synthetically generated textures were used. The camera image plane is placed parallel to the coin and light source positions are defined by their azimuth angle φ and elevation angle λ. We used eight levels of λ with eight levels of φ each to produce 64 images. The 65th image is rendered with the light placed at the camera position (i.e. λ=90°).<br> In the provided RAR-file, all the 65 images of a specific model, specularity level and texturedness level are contained in separate directories. For instance, the directory ‘texture_level0\Ref_level2\2874-back’ contains the images of the model ‘2874-back’ rendered without texture and a specularity of 0.50.</p> <p><strong>Patch Dataset</strong></p> <p>The patch dataset contains 50000 matching patch pairs for every of the 12 subsets of SIDIRE. It can be used to generate groups of feature distances by means of true and false patch pairs, in the same manner as, e.g., Matthew Brown’s <a href="http://phototour.cs.washington.edu/patches/default.htm">patch dataset</a>. Please see [1,2] for a detailed description of the evaluation scheme of patch pair databases.<br> The patches have a size of 64×64 and are arranged in images of size 3200×3200. Thus, every image contains 2500 patches where corresponding patches are placed side by side. The patches of the 12 subsets are contained in directories indicating their texture density and reflectance level, e.g. patches rendered without texture and a specularity of 0.50 are contained in the directory ‘tex0_ref2’.<br> </p> <p><strong>References</strong></p> <p>[1] Zambanini S., Kampel M. “Evaluation of Low-Level Image Representations for Illumination-Insensitive Recognition of Textureless Objects”, <em>International Conference on Image Analysis and Processing – ICIAP’13</em>, Naples, Italy, September 2013. (<a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13.pdf">pdf</a>, <a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13_supp1.pdf">supplementary material</a>)<br> [2] Brown, M., Gang Hua, Winder, S., “Discriminative Learning of Local Image Descriptors”, <em>Pattern Analysis and Machine Intelligence, </em> vol.33, no.1, pp.43-57, 2011.</p>
Dataset of light microscopy and image processing for ruthenium red staining - Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants
<p>This contains additional data relative to version 1, corresponding to a new versio of the manuscript. </p> <p>This dataset contains darkfield light microscopy images from ruthenium red stained <em>Arabidopsis thaliana </em>dark grown hypocotyls of various wildtype and mutant plants, along with the prossessing and quantified data (including segmented masks, corrected masks, raw quantification and processed quantification) reported in the study "Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants" (<a href="https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1">https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1</a>). Data was acquired following the method described in the publication. Processing of the raw data was perfomed using the RRQuant workflow (<a href="https://doi.org/10.5281/zenodo.14173186">10.5281/zenodo.14173186</a>).</p>
MIHIC: A multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification
<p>A cohort of 47 TMA sections from 114 patients was collected from Liaoning cancer hospital \& Institute, where each TMA section has the size of 188,416$\times$110,080 pixels (i.e., 42660.87um$\times$24924.15um) at 40$\times$ magnification. TMA sections contain different number of tissue cores, ranging from 28 to 48. After excluding poor quality TMA sections with tissue folding, missing or contamination, there are totally 114 patients. Each patient has tissue cores with 12 different IHC stains, including CD3, CD20, CD34, CD38, CD68, CDK4, cyclin-D1, D2-40, FAP, Ki67, P53, and SMA. Two pathologists have manually labeled clear tissue regions (i.e., without controversy) in TMA sections based on visual examination via Qupath software, where six tissue types including Alveoli, Immune cells, Nerosis, Other, Stroma, Tumor were annotated. Besides the annotated six tissue types, we added one more Background type.</p> <p>To build histological classification models, we split 309,698 image patches in MIHIC dataset into three sets: training, validation and test. Note that image patches extracted from the same annotated tissue region are distributed into the same set, which avoids data leakage during classification model optimization. According to the number of extracted ROIs, train, val and test accounted for 64\%, 16\% and 20\%.</p> <h1>if you use this dataset, please cite:</h1> <pre>@article{wang2024mihic, title={MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification}, author={Wang, Ranran and Qiu, Yusong and Wang, Tong and Wang, Mingkang and Jin, Shan and Cong, Fengyu and Zhang, Yong and Xu, Hongming}, journal={Frontiers in Immunology}, volume={15}, year={2024}, publisher={Frontiers Media SA} }</pre>
Corpus Nummorum - Coin Image Dataset
<p>Corpus Nummorum - Coin Image Dataset</p><p>This dataset is a collection of ancient coin images from three different sources: the <a href="https://www.corpus-nummorum.eu/">Corpus Nummorum (CN)</a> project, the <a href="https://ikmk.smb.museum/home?lang=en">Münzkabinett Berlin</a> and the <a href="https://www.bnf.fr/fr/departement-monnaies-medailles-antiques">Bibliothèque nationale de France, Département des Monnaies, médailles et antiques</a>. It covers Greek and Roman coins from ancient Thrace, Moesia Inferior, Troad and Mysia. This is a selection of the coins published on the <a href="https://www.corpus-nummorum.eu/search/coins">CN</a> portal (due to copyrights). </p><p>The dataset contains 115,160 images with about 29,000 unique coins. The images are split in three main folders with different assignment of the coins. Each main folder is sorted with the help fo subfolders which hold the coin images. The "dataset_coins" folder contains the coin photos divided into obverse and reverse and arranged by coin types. In the "dataset_types" folder the obverse and reverse image of the coins are concatenated and transformed to a quadratic format with black bars on the top and bottom. The images here are sorted by their coin type. The last folder "dataset_mints" contains the also concatenated images sorted by their mint. An "sources" csv file holds the sources for every image. Due to copyrights the image size is limited to 299*299 pixels. However, this should be sufficient for most ML approaches. </p><p>The main purpose for this dataset in the CN project is the training of Machine Learning based Image Recognition models. We use three different Convolutional Neural Network based architectures: VGG16, VGG19 and ResNet50. Our best model (VGG16) archieves on this dataset a 79% Top-1 and a 97% Top-5 accuracy for the coin type recognition. The mint recognition achieves an 79% Top-1 and 94% Top-5 accuracy. We have a <a href="https://github.com/Frankfurt-BigDataLab/IR-on-coin-datasets">Colab notebook</a> with two models (trained on the whole CN dataset) online.</p><p>During the summer semester 2023, we held the "Data Challenge" event at our Department of Computer Science at the Goethe-University. We gave our students this dataset with the task to achieve better results than us. Here are their experiments:</p><p><a href="https://github.com/Chantalkle/DataChallenge2023">Team 1: Voting and stacking of models</a></p><p><a href="https://github.com/Cappl1/DataChallenge">Team 2: Multimodal model</a></p><p><a href="https://github.com/cr-heidemann/Data-Challenges">Team 3: Transformer models</a></p><p><a href="https://github.com/reinhud/dc_23_new">Team 4: Dockerized TIMM Computer Vision Backend & FastAPI</a></p><ul><li>Approach | Type Dataset | Mint Dataset</li><li>Ours 79% 79%</li><li>Team 1 - 86%</li><li>Team 2 86% -</li><li>Team 3 88% 58%</li><li>Team 4 - -</li></ul><p> </p><p>Now we would like to invite you to try out your own ideas and models on our coin data.</p><p>If you have any questions or suggestions, please, feel free to contact us. </p>
Example datasets for ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images
<p>This is an example dataset.</p><p>It accompanies the manuscript titled "ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images".</p>
Whole brain imaging dataset (ASW, Butanol, Carvacrol) related to Hoyer et al
<p>Data related to the manuscript titled: Polymodal sensory perception drives robust attachment and metamorphosis of a pre-vertebrate zooplanktonic larva. </p>
Whole brain Calcium Imaging dataset (CsCl, 1mM NH4Cl, 100mM NH4Cl, SDS)
<p>Data related to the manuscript titled: Polymodal sensory perception drives robust attachment and metamorphosis of a pre-vertebrate zooplanktonic larva. </p>
TINKER_WP3_2D images - profile scans dataset_221123
<p>a subset of the 2d gap images including profile measurements along x and y axes.</p>
multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Worldwide Dataset for Image Geolocation
Open the record for dataset details and reuse information.
Image dataset for quantification of virus-infected cells
<p>The dataset contains microscopy images of hantavirus-infected cells stained with antibodies against the viral nucleocapsid protein. </p>
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.