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979 results for “image dataset”
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-panCK (epithelial cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Dataset for image-based geometric digital twinning for stone masonry elements
<p>This is the dataset used to assess the performance of the geometrical digital twinning algorithm proposed by Pantoja-Rosero et, al (2023) in the article "Image-based geometric digital twinning for stone masonry elements" (<a href="https://doi.org/10.1016/j.autcon.2022.104632">https://doi.org/10.1016/j.autcon.2022.104632</a>)</p>
Dataset for image-based geometric digital twinning for stone masonry elements - part 2
<p>This is the dataset used to assess the performance of the geometrical digital twinning algorithm proposed by Pantoja-Rosero et, al (2023) in the article "Image-based geometric digital twinning for stone masonry elements" (<a href="https://doi.org/10.1016/j.autcon.2022.104632">https://doi.org/10.1016/j.autcon.2022.104632</a>)</p>
Raw dataset for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>The dataset contains raw diffraction images in .tiff format. Each image file name contains three numbers, separated by "_". The first number refers to the order in which the images were taking in laboratory time. The second number refers to the absolute translation stage position in millimeters in the optical beam path of the pump beam. The stage position can be converted into a pump-probe delay time (taking into account the speed of light and a factor of 2 for the beam path, since the pulses move back and forth on the stage). Larger stage position values correspond to the optical pump pulse arriving later with respect to the probe pulse. Time zero was determined to be at 156.26 mm using a solid reference sample.</p>
(11)-Strobl2023A-DS0001--0010 – Ten Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(11)-Strobl2023A-DS0001--0010 – Ten <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Image dataset from multiplex IHC stained TMA sections
<p><strong>IHC Cohort:</strong> Multiplex IHC stained histological slides were collected from Liaoning Cancer Hospital and Institute in China. The raw data collected are TMA sections, all of which are obtained from NSCLC patients. Non-overlapping image patches (256*256 pixels) are extracted from TMA sections and manually annotated by our collaborated pathologists via the Qupath software. Initially, seven TMA sections with multiplex stains including CD3, CD20, CD38, CDK4, Cyclin-D1, Ki67, and P53 were used. Except for CD3 stained TMA sections, we randomly cropped 25 image patches from each of these TMA sections, of which 17, 3 and 5 patches are correspondingly used for training, validation, and testing. Note that 18, 3 and 5 CD3 image patches are cropped for building dataset. To suppress over-fitting and enhance generalization of the SRSA-Net, we additionally included 81 annotated image patches from other five TMA sections with stains of CD34, CD68, D2-40, FAP, and SMA into training and validation set. In total, the IHC cohort includes 9,725 manually identified cell nuclei. The numbers of training, validation and testing image patches are 195, 36, and 35, respectively.</p> <p>For the masks, the first channel and the second channel denotes the negative and positive nuclei pixels, respectively. Note that each pixel is labelled from 0 to n, where n is the number of individual nuclei detected. 0 pixels indicate background. Pixel values i indicate that the pixel belongs to the ith nucleus. The last channel marks the information of all the nuclei pixels, where nuclei pixel are left as 0, otherwise 1.</p> <p><strong>fold1: training set</strong></p> <p><strong>fold2: validation set</strong></p> <p><strong>fold3: testing set</strong></p> <p> </p> <h2>if you use this dataset, please cite:</h2> <pre>@article{wang2024simultaneously, title={Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections}, author={Wang, Ranran and Qiu, Yusong and Hao, Xinyu and Jin, Shan and Gao, Junxiu and Qi, Heng and Xu, Qi and Zhang, Yong and Xu, Hongming}, journal={Biomedical Signal Processing and Control}, volume={93}, pages={106143}, year={2024}, publisher={Elsevier} }</pre>
Data from: Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets
<p>High temperature induced diamond degradation often leads to the failure of diamond tools. In this work, diamond samples holding different degrees of thermal damage were prepared by heating and sintering. The influence of diamond particle size and processing temperature was investigated through mechanical testing and micromorphology observation, meanwhile, a dataset containing 2870 SEM images showing diamonds with different degrees of degradation was constructed. By modification of VGG16 network, classification models and regression models were developed for thermal damage evaluation and sample property prediction. Training strategies including transfer learning and data augmentation were implemented and verified essential on the small dataset, where drop-out showed no positive effects. Two classification models (3-class and 65-class) were constructed and trained for damage evaluation. Visualized damage feature maps exported from Grad-CAM revealed the influential mechanism of thermal damage on diamonds, which proved the effectiveness of the classification models as well. Under the optimized training strategies, regression models were built for sample property prediction. The models towards toughness index, bending strength loss, relative density and Rockwell hardness were examined. Comparing the output results with real property values in test sets, the first two models matched well, and the latter two showed the opposite. It verified the validity of the regression models for property prediction as they were all established based on diamond damage image datasets. The loss in bending strength loss prediction model was smaller than that of toughness index, indicating bending strength easier to be shorten than impact toughness for diamond/metal composites suffering thermal impacts.</p>
Assessment of Subsampling Schemes for Compressive Nano-FTIR Imaging: Underlying Dataset
<p>The dataset in this publication is related to the following Publication:</p> <p>Metzner, S., Kästner, B., Marschall, M., Wubbeler, G., Wundrack, S., Bakin, A., Hoehl, A., Ruhl, E., & Elster, C. (2022).<br> Assessment of Subsampling Schemes for Compressive Nano-FTIR Imaging.<br> <em>IEEE Transactions on Instrumentation and Measurement</em>, <em>71</em>, 1–8.<br> https://doi.org/10.1109/TIM.2022.3204072</p> <p>It contains the underlying code and the data for generating the figures.</p>
Dataset for "Segmentation of Lipid Droplets in Histological Images"
<p>Datasets for the publication "Segmentation of Lipid Droplets in Histological Images" (Medical Imaging with Deep Learning (MIDL 2023), short paper track. 2023. https://openreview.net/forum?id=nTnAm_El0RC)</p>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
SRDTrans dataset: simulated calcium imaging data at different imaging speeds
<p>SRDTrans dataset: simulated calcium imaging data at different imaging speeds</p>
Sample datasets for review of "Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3"
<p>2 sample .tiff stacks and desired output files for use in evaluating the algorithm described in "Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3"</p>
Hypocenter-based 3D Imaging of Active Faults: Method and Applications in the Southwestern Swiss Alps [Dataset]
<p>Data repository to the JGR publication of Truttmann et al. (2023), including following datasets for the two analyzed earthquake sequences (St. Léonard and Anzère)</p> <p>- Interactive 3D fault-network models</p> <p>- Focal mechanisms</p> <p>- Relocated hypocenter locations (hypoDD)</p> <p>- Station networks</p> <p>- hypoDD parameters</p>
Training image dataset of strawberry flowers for new Nature Inspired Detector (NID)
<p>This dataset consists of images of 4 white-flowering strawberry cultivars: <em>F. x ananassa</em> <em>‘Seascape’, ‘Fort Laramie’,</em> and <em>'Hecker',</em> and<em> F. Vesca. </em>The image's size is 416x416 with the following augmentations: horizontal and vertical flip, rotate ±90-degrees, rotate ±15-degrees, sheer ±15-degrees vertical and horizontal, noise 5%, and blur 5px. Labels for both Faster R-CNN and Yolo V5 are included. </p>
Supplementary movies and datasets of the paper: Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP
<p>Imaging data supporting the paper: </p> <p>Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP, containing raw and processed data from fluorescent and electron microscopy.</p> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </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.