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Dataset results
2,007 results for “Image Studies”
Functional Respiratory Imaging Study
ClinicalTrials.gov study NCT04876677. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Study of Innovative Multimodal Imaging Biomarkers to Predict Anatomical Outcome in Naive Patients With wAMD Treated With Brolucizumab.
ClinicalTrials.gov study NCT04774926. IPD Sharing: YES. Countries: 1. Publications: 1.
Clinical Effectiveness of Body Fat Distribution Imaging in Real-World Practice: The BODY-REAL Study
ClinicalTrials.gov study NCT04763772. IPD Sharing: YES. Countries: 1. Publications: 1.
Synergistic label-free fluorescence imaging and miRNA studies reveal dynamic human neuron-glial metabolic interactions following injury
Open the record for dataset details and reuse information.
BIMAGES: Bivalve images for morphological analysis and genetic estimation study
Open the record for dataset details and reuse information.
Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101
<p>Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies.</p> <p>Operational conditions of the analysis equipment were the following:</p> <p>- The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater.</p> <p>- The EDS analysis were collected as average on 5 points per each SEM picture</p> <p>- A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied</p>
Data from: Thalamus and focal to bilateral seizures: a multi-scale cognitive imaging study
<p><span><span><span><span><span><span><span><span><span><span><span><b><i>Objective</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>To investigate the functional correlates of recurrent secondarily generalized seizures in temporal lobe epilepsy (TLE), using task-based fMRI as a framework to test for epilepsy-specific network rearrangements. As the thalamus modulates propagation of temporal-lobe onset seizures and promotes cortical synchronization during cognition, we hypothesized that occurrence of secondarily generalized, i.e. focal to bilateral tonic-clonic seizures (FBTCS), would relate to thalamic dysfunction, altered connectivity and whole-brain network centrality.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Methods</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>FBTCS occur in a third of patients with TLE and are a major determinant of disease severity. In this cross-sectional study, we analyzed 113 patients with drug-resistant TLE (55 left/58 right), who performed a verbal fluency fMRI task that elicited robust thalamic activation. Thirty-three patients (29%) had experienced at least one FBTCS in the year preceding the investigation. We compared patients with TLE-FBTCS to those without FBTCS via a multi-scale approach, entailing analysis of SPM12-derived measures of activation, task-modulated thalamic functional connectivity (psychophysiological interaction), and graph-theoretical metrics of centrality. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Results</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Individuals with TLE-FBTCS had less task-related activation of bilateral thalamus, with left-sided emphasis, and left hippocampus than those without FBTCS. In TLE-FBTCS, we also found greater task-related thalamotemporal and thalamo-motor connectivity, and higher thalamic degree and betweenness centrality. Receiver operating characteristic curves, based on a combined thalamic functional marker, accurately discriminated individuals with and without FBTCS.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Conclusions</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>In TLE-FBTCS, impaired task-related thalamic recruitment coexists with enhanced thalamotemporal connectivity and whole-brain thalamic network embedding. Altered thalamic functional profiles are proposed as imaging biomarkers of active secondary generalization.</span></span></span></span></span></span></span></span></span></span></span></p>
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
<p><span>Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely <a name="_Hlk56106607">distinguishable</a>. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is <a name="_Hlk48748756">based on the state-of-the-art U-Net</a>, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. </span></p> <p><span>Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.</span></p>
SiEUGreen - Data for 'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'
<p>Dataset used in the scientific publication <a href="https://zenodo.org/record/4671852">'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'</a>. The data includes chemical contents of tomatoes that was measured, images and scripts used in the paper. The scripts here aim to predict tomato quality parameters, sugar content, acidity, sugar acid ratio and lycopene, of automatically segmented tomato through hyperspectral image reconstruction from single RGB image. The same data can also be found at the <a href="https://github.com/ZJiangsan/TomatoQualityPredictionOnAutomaticallySegmentedTomato">Github repository</a>. The data collection and scientific paper was produced by SiEUGreen partners at Norwegian Institute of Bioeconomy Research (NIBIO).</p>
Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires
<p>This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (R2=0.878), surpassing existing methods in spatial resolution and performance. </p> <p>Inside the Replication Package folder, the user can replicate the main results from the paper. This includes:</p> <ol> <li> <p><strong>Small Area Estimation (SAE) Replication:</strong></p> <ul> <li> <p><strong>Argentina Household Survey Data (EPH):</strong> Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta).</p> </li> <li> <p><strong>Argentina Census Microdata:</strong> Raw 2010 census microdata (censo2010_fullraw_p.dta).</p> </li> <li> <p><strong>Census Tract Map:</strong> Shapefile of 2010 census tracts (radios_eph_with_link.shp).</p> </li> <li> <p><strong>SAE Output:</strong> The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model.</p> </li> </ul> </li> <li> <p><strong>CNN-based Income Prediction Replication (Paper Results):</strong></p> <ul> <li> <p><strong>CNN Model Income Predictions:</strong> Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp).</p> </li> <li> <p><strong>Normalization Scalars:</strong> A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos).</p> </li> <li> <p><strong>World Settlement Footprint (WSF):</strong> Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas.</p> </li> </ul> </li> </ol> <p>Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif.</p> <p>Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below.</p> <p>For researchers who wish to replicate the full analysis pipeline starting from the original source imagery, the data must be acquired commercially. The proprietary Pleiades and Pleiades NEO satellite imagery is owned by Airbus and can be purchased through their data portal: https://space-solutions.airbus.com/imagery/. To facilitate this process, we provide the unique product identifiers for each scene used in this study. These identifiers can be used to query the Airbus archive and purchase the exact scenes.</p> <ul> <li><strong>Pléiades</strong>: for 2013 imagery the IDs are DS_PHR1A_201302051411520_FR1_PX_W059S35_0807_03124, DS_PHR1A_201302071357305_FR1_PX_W059S35_0410_06105 and DS_PHR1A_201302071357509_FR1_PX_W059S35_0609_05426, and for 2018, DS_PHR1A_201803251356358_FR1_PX_W059S35_0909_03875, DS_PHR1A_201808021356574_FR1_PX_W059S35_0509_06938 and DS_PHR1A_201808021357186_FR1_PX_W059S35_0706_06104.</li> <li><strong>Pleiades NEO</strong>: for 2022 imagery the IDs used are 000047717_1_22_STD_A, 000047717_1_24_STD_A, 000047717_1_25_STD_A, 000047717_1_26_STD_A, 000058605_1_3_STD_A, 000058605_1_4_STD_A, 000058605_1_7_STD_A, and 000058608_1_2_STD_A.</li> </ul> <p><strong>Important Usage Note:</strong> Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision.</p> <p>Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.</p> <p>Data can be visualized by accessing: <a href="https://ingresoamba.netlify.app">https://ingresoamba.netlify.app</a></p> <p> </p> <p> </p>
Activated Carbon Cloth Electrodes for Capacitive Deionization: A Neutron Imaging Study
<p>Neutron images of capacitive deionization by activated carbon cloths.</p><p>Corresponding readouts from potentiostat.</p><p>Nitrogen gas adsorption data of the activated carbon cloths.</p>
Five-fold training dataset of fossil pollen images from Burgäschisee used for automated fossil pollen identification (von Allmen et al. study)
<p>This dataset consists of a training dataset for the CNN model containing pollen grain images of nine common pollen taxa, one marker class (Lycopodium clavatum) and four abundant non-pollen debris classes. The dataset was split five-times so that each image is part of the validation dataset in just one of these splits. Additionally, the dataset contains annotated images used to train the object detection model and a dataset of images that were used to evaluate the performance of the CNN model. For further information on the datsets themselves and how they were used the reader may refer to the github repository here attached (<a href="https://github.com/RobinVonAllmen/FOSSILPOLLEN">https://github.com/RobinVonAllmen/FOSSILPOLLEN)</a></p>
Data from: Multi-image flock size estimation with CountEm: A case study with half a million Common Eiders and Greater Snow Geese.
<p>The present data set is related to the manuscript "Multi-image flock size estimation with CountEm: A case study with half a million Common Eiders and Greater Snow Geese" submitted to the Ecosphere journal.</p> <p>The files COEI_data.csv and GSGO_data.csv contain data and results of the 179 COEI and 99 GSGO ECA Flocks images respectively. The files have one row per image. Both files have 9 columns corresponding to the following variables:</p> <ul> <li> “Filenumber” is used to identify the number of the corresponding image, namely “001” to “099” for GSGO, and “001” to “179” for COEI.</li> <li> “N”: Total number of annotated birds in the image.</li> <li> “Nest”: Bird number estimation (bN 439 ) obtained with a single real mode CountEm run.</li> <li> “CE_sim”: Empirical coefficient of error (relative standard error, CEe(bN 440 )) obtained from 2000 simulated measurements in simulation mode.</li> <li> “f”: Sampling fraction used in the real mode CountEm run.</li> <li>“n0”: Initial number of quadrats used in the real mode CountEm run.</li> <li>“Q”: Total number of birds (i.e. sample size) counted in the real mode CountEm estimation.</li> <li> “n”: Number of non-empty quadrats counted in the real mode CountEm estimation.</li> <li> “UserTime”: Counting time (in minutes) in the real mode CountEm estimation.</li> <li> “ManualTime”: Counting time (in minutes) of the manual annotation process with ImageJ.</li> </ul>
Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia's Northwest Shelf; supplementary material
<p>This dataset comprises two supplementary materials. Supplementary Materials A includes seismic processing workflows conducted by industry on the seismic lines used in this study. The seismic processing workflows are not the property of the author but are publicly available on the NOPIMS and WAPIMS databases. Collating these workflows into supplementary materials provides a simple method for readers to access material important for this research paper. Supplementary Materials B is a collection of 2D seismic lines the author conducted stratigraphic horizon mapping on for this study as viewed in 3D. More details on this dataset can be found throughout the research paper "Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia’s Northwest Shelf".</p>
Data from "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-au Environment'
<p>Reduced data from Huang et al., 2022, "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-astronomical-unit Environment,' ApJ, 930, 171 (arXiv:2204.01758). </p> <p>See Table 1 of the article for the corresponding observing program codes and attributions for archival data (if applicable). </p> <p><strong>Images:</strong></p> <p>DOTau_12CO_automask.image.pbcor.fits: 12CO J=2-1 image cube<br> DOTau_12CO_automask.mom1.fits: 12CO J=2-1 moment 1 map<br> DOTau_12CO_automask.pbcor.2sigcut.mom0.fits: 12CO J=2-1 moment 0 map<br> DOTau_13CO_automask.image.pbcor.fits: 13CO J=2-1 image cube<br> DOTau_13CO_automask.mom1.fits: 13CO J=2-1 moment 1 map<br> DOTau_13CO_automask.pbcor.2sigcut.mom0.fits: 13CO J=2-1 moment 0 map<br> DOTau_C18O_automask.image.pbcor.fits: C18O J=2-1 image cube<br> DOTau_C18O_automask.pbcor.2sigcut.mom0.fits: C18O J=2-1 moment 0 map<br> DOTau_C18O_automask.pbcor.mom1.fits: C18O J=2-1 moment 1 map<br> DOTau_cADI_average.fits: SPHERE H-band cADI image (pixel scale: 0.01225 arcseconds)<br> DOTau_CS_automask.image.pbcor.fits: DO Tau CS J=5-4 image cube<br> DOTau_CS_automask.mom1.fits: DO Tau CS J=5-4 moment 1 map<br> DOTau_CS_automask.pbcor.2sigcut.mom0.fits: DO Tau CS J=5-4 moment 0 map<br> DOTau_DoLP.fits: DO Tau degree of linear polarization map (pixel scale: .0245 arcseconds)<br> DOTau_IDF-RDI.fits: SPHERE total intensity image produced with IDF-RDI (pixel scale: 0.01225 arcseconds)<br> DO-TAU_NICMOS_F110W_MRDILib-18_KL-2_Pixel.fits: HST NICMOS F110W image (pixel scale: 0.075 arcseconds)<br> DO-TAU_NICMOS_F160W_MRDILib-100_KL-1_Pixel.fits: HST NICMOS F160W image (pixel scale: 0.075 arcseconds)<br> DOTau_Qphi_average.fits: SPHERE H-band Qphi image (pixel scale: 0.01225 arcseconds)<br> DO_Tau_STIS_KlipWithin160pixel_counts_s_pixel.fits: HST STIS image (pixel scale: 0.0507 arcseconds)</p> <p><strong>Measurement sets:</strong></p> <p>DOTau_12CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 12CO J=2-1 visibilities<br> DOTau_13CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 13CO J=2-1 visibilities<br> DOTau_C18O.ms.contsub.tar: Self-calibrated, continuum-subtracted C18O J=2-1 visibilities<br> DOTau_CS.ms.contsub.tar: Self-calibrated, continuum-subtracted CS J=5-4 visibilities</p> <p><strong>Scripts:</strong></p> <p>DOTau_1.1mmreduction.py: CASA self-cal and imaging script for CS data <br> DOTau_1.3mmreduction.py: CASA self-cal and imaging script for CO data </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>
Real-time magnetic resonance imaging to study orthostatic intolerance mechanisms in human beings: Proof of concept
<p>This dataset was acquired at DLR, Cologne, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee. All subjects gave written informed consent.</p> <p> </p> <p><strong>Data acquisition</strong><br> <em>MRI data</em></p> <p>All MR images were acquired on a Siemens 3T mMR Biograph using cardiac, spine and head coils. The scanning protocol consisted of the following sequences.</p> <ul> <li><strong>MPI70_SA20_FOV320_16X6MM_33MS</strong>: cardiac real-time MRI of the short axis (TR=2.56 ms; TE=1.62 ms; FA= 10°; 1.6x1.6 mm; 6 mm slice thickness; 20 slices; FoV320x320 mm; radial spokes 13)</li> <li><strong>MPI70_TP_PCMV100_FOV320_15X6MM_33MS</strong>: blood flow real-time MRI of the pulmonary trunk (TR=3.33 ms; TE=2.24 ms; FA= 10°; 1.5x1.5 mm; 6 mm slice thickness; FoV=320x320 mm; VENC=100 cm/s; radial spokes 5)</li> <li><strong>MPI70_PCMV100_FOV192_075X6MM_80MS:</strong> blood flow real-time MRI of the middle cerebral artery (TR=4.44 ms; TE=3.10 ms; FA= 12°; 0.75x0.75 mm; 6 mm slice thickness; FoV=192x192 mm; VENC=100 cm/s; radial spokes 9)</li> </ul> <p> </p> <p>Cinematic real-time MRI of the short axis view and blood flow measurements of the pulmonary trunk and in the left and right middle cerebral artery were acquired at baseline and during -30mmHg LBNP.</p> <p> </p> <p>Video files:</p> <p><a href="https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <p><a href="https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <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>
Deep learning based Image Compression for Microscopy Images: An Empirical Study
<p>This dataset supports the manuscript titled "Deep Learning Based Image Compression for Microscopy Images: An Empirical Study." The materials provided are essential for replicating the experiments in the study. The dataset is divided into three main parts:</p> <ol> <li>Config: train and test yaml config files for both 2D and 3D mmv_im2im labelfree task</li> <li>Sample_data: sample data to test the compression and the downstream labelfree code, in both 2D and 3D case.</li> <li>Model: fine-tuned 2D/3D labelfree model and 3D compression model. We use pre-trained 2D compression models from CompressAI</li> </ol> <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.