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979 results for “image dataset”
Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta
<p>The ratio of Na<sup>+</sup> and K<sup>+</sup> is an important determinant of the magnitude of Na<sup>+</sup> toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na<sup>+</sup> and K<sup>+</sup> levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na<sup>+</sup> exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m<sup>-1</sup>. Spectral data was used to create models for Na<sup>+</sup> and K<sup>+</sup> prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R<sup>2</sup>. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.</p>
Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle"
<p>Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle".<br> The PDF file entitled "Supplementary material" contains the uncropped original western blots and autoradiographs published in the article.<br> The PDF files entitled "Extended Data Fig.2,6,7,9,10 all panels" and "Figure 1,4 all panels" contain the original full-size confocal immunofluorescence images from which specific ROIs are published in the article.<br> The Excel files "Source Data Principal Figures" and "Source Data Extended Figures" contain all the original datasets used for calculation and graphical representation of data published in the article.</p>
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
LMRG Image Analysis Study - FISH datasets
<p>Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D confocal fluorescence images of sub-diffraction punctate staining (fluorescence in situ hybridization (FISH) in <em>C. elegans</em>).</p> <p>See https://github.com/ABRFLMRG/image-analysis-study for more details.</p>
LMRG Image Analysis Study - nuclei datasets
<p>Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D widefield fluorescence images of nuclei.</p> <p>See https://github.com/ABRFLMRG/image-analysis-study for more details.</p>
Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas
<p><strong>The Dataset</strong></p> <p>A collection of images of parking lots for <em>vehicle detection, segmentation, and counting</em>.<br> Each image is <em>manually</em> labeled with pixel-wise masks and bounding boxes localizing vehicle instances.<br> The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars.<br> The main peculiarity is that <em>images are taken during the day and the night</em>, showing utterly different lighting conditions.</p> <p>We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night.<br> In line with these splits we provide some annotation files:</p> <ul> <li> <p><em>train_coco_annotations.json</em> and <em>val_coco_annotations.json</em> --> JSON files that follow the golden standard MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category<em> 'car'</em>. They are suitable for vehicle detection and instance segmentation.</p> </li> <li> <p><em>train_dot_annotations.csv</em> and <em>val_dot_annotations.csv</em> --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.</p> </li> <li> <p><em>ground_truth_test_counting.csv</em> --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.</p> </li> </ul> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{Ciampi_visapp_2021, doi = {10.5220/0010303401850195}, url = {https://doi.org/10.5220%2F0010303401850195}, year = 2021, publisher = {{SCITEPRESS} - Science and Technology Publications}, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {Domain Adaptation for Traffic Density Estimation}, booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_ndispark_6560823, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}}, month = may, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6560823}, url = {https://doi.org/10.5281/zenodo.6560823} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p>
Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the Validation Image part. </p> <p>If you use this dataset in your research, you must cite the papers in the References below !!!</p>
Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data
<p>This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called Recurrent All-Pairs Field Transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. IWe consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments included in this dataset shed new light on the capabilities of deep learning for PIV processing. This dataset is given as binary TFRECORD format.</p>
Dataset for determining crack kinematics from imaged crack patterns
<p>This is the dataset used to assess the performance of the crack kinematics algorithm proposed by Pantoja-Rosero et, al (2022) in the article "Determining crack kinematics from imaged crack patterns".</p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
<p><strong>LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters</strong></p> <p><strong>Usage</strong></p> <ul> <li>Download the compressed files (13 in total) and uncompress them</li> <li><strong>Please cite reference 1) below in your publications if you make use of the data of this repository</strong></li> </ul> <p><strong>People & Contact</strong></p> <ul> <li><a href="http://wiki.hh.se/caisr/index.php/Fernando_Alonso-Fernandez">Fernando Alonso-Fernandez</a> (contact person).</li> </ul> <p><strong>References</strong></p> <ol> <li>Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., Alonso-Fernandez, F., "On the Effect of Selfie Beautification Filters on Face Detection and Recognition" <a href="https://github.com/HalmstadUniversityBiometrics/LFW-Beautified/blob/main">https://arxiv.org/abs/2110.08934</a></li> <li>Hedman, P., Skepetzis, V., The Effect of Beautification Filters on Image Recognition: "Are filtered social media images viable Open Source Intelligence?" Master Thesis at Halmstad University, Sweden (Master’s Programme in Network Forensics) <a href="http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799">http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799</a></li> </ol> <p> </p>
Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene_experimental dataset
<p>This dataset contains the raw unprocessed data for Piastek et al., Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene, <em>J. Phys. Chem. C</em> 2022, 126, 9, 4508–4514. </p>
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
<p>NXTensor extraction library, experiment code, tropical cyclone and background images and their metadata generated from the meterological reanalysis ERA5 and MERRA-2 according to the HURDAT2 cyclone tracks.</p> <p>Version specifications:</p> <ul> <li>NXTensor: v0.3.3.10</li> <li>Experiment code: v2.0.3</li> <li>Image sets: v1</li> </ul> <p> </p>
WHUS2-CR, a thin cloud removal dataset for Sentinel-2 images
<p>WHUS2-CR is a thin cloud removal dataset for Sentinel-2A images. WHUS2-CR contains 36 paired cloud and corresponding clear Sentinel-2A images evenly distributed over the world.</p> <p><strong>Because the max storage limitation of one dataset is 50 GB, 5 files can not be uploaded on this dataset. They can be found on : <a href="https://doi.org/10.5281/zenodo.5616753">https://doi.org/10.5281/zenodo.5616753</a>. (The reported results in ref [1] are in CRMSS-result.rar, CRMSS-10-4.rar and CRMSS-4-4.rar, respectively.)</strong></p> <p><strong>The dataset reproducing code and model source code for ref [2] are on :</strong> <a href="https://github.com/Neooolee/WHUS2-CR">https://github.com/Neooolee/WHUS2-CR</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020, <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p><br> [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>The training and testing image list used in reference [2] is (The training and testing small patches are listed in <a href="https://zenodo.org/api/files/bae57b05-f2cc-4729-b97c-eec7c215e9e4/filenos.xlsx">filenos.xlsx</a>):</p> <p>Training set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160403T030602_N0201_R075_T50TMK_20160403T031209</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160413T031632_N0201_R075_T50TMK_20160413T031626</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181111T053041_N0207_R105_T43RGM_20181111T083104</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181121T053121_N0207_R105_T43RGM_20181121T091419</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160925T104022_N0204_R008_T32ULB_20160925T104115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160915T104022_N0204_R008_T32ULB_20160915T104018</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160528T153912_N0202_R011_T18TWL_20160528T154746</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160518T155142_N0202_R011_T18TWL_20160518T155138</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181208T170701_N0207_R069_T14QMG_20181208T202913</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181218T170711_N0207_R069_T14QMG_20181218T203015</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180809T190911_N0206_R056_T10UFB_20180810T002400</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180819T190911_N0206_R056_T10UFB_20180820T002955</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190306T132231_N0207_R038_T22KHV_20190306T164115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190224T132231_N0207_R038_T22KHV_20190224T164104</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181012T084901_N0206_R107_T37VCC_20181012T110218</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181022T085011_N0206_R107_T37VCC_20181022T110901</td> </tr> <tr> <td>9</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190619T023251_N0207_R103_T50JKP_20190619T071925</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190629T023251_N0207_R103_T50JKP_20190629T053618</td> </tr> <tr> <td>10</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190901T032541_N0208_R018_T47NPF_20190901T070148</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190911T032541_N0208_R018_T47NPF_20190911T084555</td> </tr> <tr> <td>11</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160419T083012_N0201_R021_T36RUU_20160419T083954</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160409T083012_N0201_R021_T36RUU_20160409T084024</td> </tr> <tr> <td>12</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190218T143751_N0207_R096_T19HCC_20190218T175945</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190208T143751_N0207_R096_T19HCC_20190208T180253</td> </tr> <tr> <td>13</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180609T061631_N0206_R034_T42TWL_20180609T081837</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180530T061631_N0206_R034_T42TWL_20180530T082050</td> </tr> <tr> <td>14</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191111T025941_N0208_R032_T49QGF_20191111T055938</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191101T025841_N0208_R032_T49QGF_20191101T054434</td> </tr> <tr> <td>15</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190818T103031_N0208_R108_T31SEA_20190818T124651</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190808T103031_N0208_R108_T31SEA_20190808T124427</td> </tr> <tr> <td>16</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191202T105421_N0208_R051_T29PPP_20191202T112025</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191212T105441_N0208_R051_T29PPP_20191212T111831</td> </tr> <tr> <td>17</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190919T074611_N0208_R135_T35JPL_20190919T105208</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190929T074711_N0208_R135_T35JPL_20190929T100745</td> </tr> <tr> <td>18</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190725T142801_N0208_R053_T20LMR_20190725T175149</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190804T142801_N0208_R053_T20LMR_20190804T175038</td> </tr> <tr> <td>19</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T043931_N0208_R033_T46TDK_20191101T074915</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191022T043831_N0208_R033_T46TDK_20191022T063301</td> </tr> <tr> <td>20</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160509T065022_N0202_R020_T41UNV_20160509T065018</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160519T064632_N0202_R020_T41UNV_20160519T064833</td> </tr> <tr> <td>21</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191019T012631_N0208_R131_T53LKF_20191019T030531</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191029T012721_N0208_R131_T53LKF_20191029T040003</td> </tr> <tr> <td>22</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190503T071621_N0207_R006_T38PMB_20190503T092340</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190423T071621_N0207_R006_T38PMB_20190423T093049</td> </tr> <tr> <td>23</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190724T011701_N0208_R031_T56VLM_20190724T031136</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190714T011701_N0208_R031_T56VLM_20190714T031656</td> </tr> <tr> <td>24</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181020T012651_N0206_R074_T54TXN_20181020T032526</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181010T012651_N0206_R074_T54TXN_20181010T055606</td> </tr> <tr> <td>25</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20170224T162331_N0204_R040_T16REV_20170224T162512</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20170214T162351_N0204_R040_T16REV_20170214T163022</td> </tr> <tr> <td>26</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190613T032541_N0207_R018_T49UFT_20190613T062257</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190623T032541_N0207_R018_T49UFT_20190623T061953</td> </tr> <tr> <td>27</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190208T011721_N0207_R088_T53KLP_20190208T024521</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190129T011721_N0207_R088_T53KLP_20190129T024501</td> </tr> <tr> <td>28</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190530T184921_N0207_R113_T12VVN_20190530T222535</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190520T184921_N0207_R113_T12VVN_20190520T222900</td> </tr> </tbody> </table> <p>Testing set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20150826T084006_N0204_R064_T37UCQ_20150826T084003</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20150905T083736_N0204_R064_T37UCQ_20150905T084002</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T000241_N0208_R030_T56HLH_20191101T012241</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191111T000241_N0208_R030_T56HLH_20191111T012137</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190711T174911_N0208_R141_T13TEE_20190711T212846</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190701T174911_N0207_R141_T13TEE_20190701T212910</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190201T093221_N0207_R136_T32PRR_20190201T113425</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190211T093121_N0207_R136_T32PRR_20190211T103706</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190314T021601_N0207_R003_T52SCF_20190314T055026</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190304T021601_N0207_R003_T52SCF_20190304T042035</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180804T045701_N0206_R119_T46VDH_20180804T065907</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180725T045701_N0206_R119_T46VDH_20180725T065359</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190707T213531_N0207_R086_T05VPJ_20190707T231819</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190627T213531_N0207_R086_T05VPJ_20190628T010801</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190605T125311_N0207_R052_T24MXV_20190605T160555</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190615T125311_N0207_R052_T24MXV_20190615T142536</td> </tr> </tbody> </table>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Dataset for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM"
<p>The dataset includes root files used in generating the main figures for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM" by Jana Vasiljević, Alf Göök and Bo Cederwal. The article will be submitted to MDPI Applied Sciences.</p> <p> </p>
Converting Pixel into millimeter in ultrasound images: Technique and dataset
<p>A new dataset available for the public research community; our dataset includes 2835 images representing three fetal head plans (Trans-cerebellum, Trans-thalamic, and Trans-ventricular). Further, the dataset is large and more diverse regarding fetal planes in various gestational ages (GA). Table 1. provides a descriptive analysis of the dataset that includes the number of samples (N), mean, median, standard deviation (S), the minimum and maximum pixel value in mm, and three percentiles.</p> <p><strong>Please cite the original conference paper of this work.</strong></p> <table align="center"> <thead> <tr> <th> <p><strong>Table 1</strong>: Descriptive analysis for our dataset</p> </th> </tr> </thead> </table> <table align="center"> <thead> <tr> <th> <p><strong>N</strong></p> </th> <th> <p><strong>Mean</strong></p> </th> <th> <p><strong>Median</strong></p> </th> <th> <p><strong>SD</strong></p> </th> <th> <p><strong>Minimum</strong></p> </th> <th> <p><strong>Maximum</strong></p> </th> <th> <p><strong>25th</strong></p> </th> <th> <p><strong>50th</strong></p> </th> <th> <p><strong>75th</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>2835</p> </td> <td> <p>0.144</p> </td> <td> <p>0.130</p> </td> <td> <p>0.0441</p> </td> <td> <p>0.0600</p> </td> <td> <p>0.330</p> </td> <td> <p>0.110</p> </td> <td> <p>0.130</p> </td> <td> <p>0.180</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Datasets related to the paper " Inception Models for Fashion Image Captioning: An Extensive Study on Multiple Datasets"
<p>This collection contains three datasets in HDF5 format: FashionCap, ReducedInFashAI, ReducedFACAD.</p>
SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images
<p>Optical chemical structure recognition (OCSR) systems aim to extract the molecular structure information, usually in the form of molecular graph or SMILES, from images of chemical molecules. While many tools have been developed for this purpose, challenges still exist due to different types of noises that might exist in the images. Specifically, we focus on the “arrow-pushing” diagrams, a typical type of chemical images to demonstrate electron flow in mechanistic steps. We present Structural molecular identifier of Molecular images in Chemical Reaction Mechanisms (SMiCRM), a dataset designed to benchmark machine recognition capabilities of chemical molecules with arrow-pushing annotations. Comprising 453 images, it spans a broad array of organic chemical reactions, each illustrated with molecular structures and mechanistic arrows. SMiCRM offers a rich collection of annotated molecule images for enhancing the benchmarking process for OCSR methods. This dataset includes a machine-readable molecular identity for each image as well as mechanistic arrows showing electron flow during chemical reactions. It presents a more authentic and challenging task for testing molecular recognition technologies, and achieving this task can greatly enrich the mechanisitic information in computer-extracted chemical reaction data.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.