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979 results for “image dataset”
Dataset of live imaging of RNA Polymerase II CTD phosphorylation in zebrafish embryos
<p>Images are recruited RNA polymerase II Serine 5 (Pol II Ser5P) and RNA polymerase II Serine 2 (Pol II Ser2P) in live sphere stage zebrafish embryos, visualized with antibody fragments (Fab) labelled with Janelia Fluor 647 and Alexa Fluor 488, respectively.<br> <br> Dataset "<strong>EmbryoC_20ms_040.nd2" </strong>is recorded by 20 ms exposure time and contains 120 frames. Dataset "<strong>EmbryoC_50ms_042.nd2</strong>" contains images recorded with 50 ms exposure time and contains 61 frames. </p>
Dataset for "Deriving map images of generalised mountain roads with generativeadversarial networks."
<p>This is the dataset used in the paper "Deriving map images of generalised mountain roads with generativeadversarial networks.". The data are derived from an extract of the database used to make the topographic maps at the 1:25,000 scale and 1:250,000 map scale at IGN.</p> <p>The base vector data are presented in shapefile_montain_road folder, the vector manually matched data are in shapefile_manually_matched folder; finally constructed images using manually paired data and a reasonable fixe size grid are in roads_images_manually_matched folder. </p>
Dataset and 3D Vs Model for "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography"
<p>Final 3D Vs model and dispersion data for the paper entitled "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography".</p> <p>In the vel_files folder, there are 10 files for each depth for 1-15 km. The format of each velocity file is as follows:</p> <p>Column Value<br> 1 Lattitude (°)<br> 2 Longitude (°)<br> 3 Vs (km/s)</p> <p>The format of the dispersion data is as follows (See <a href="https://www.eas.slu.edu/eqc/eqc_cps/TUTORIAL/EMPIRICAL_GREEN/example1.html">Computer Programs in Seismology Tutorials - do_mft</a> for more information on the format):</p> <p>Column Value<br> 1 Type of file, MFT96<br> 2 Wave type: R for Rayleigh <br> 3 Dispersion type: U for group velocity<br> 4 Mode: 0 represents the fundamental mode<br> 5 Filter period, T, in seconds<br> 6 Dispersion value, either group or phase<br> 7 Error in dispersion. This is just a place holder since there is no way to estimate an error from a single trace. The group velocity error is determined from the ratio of the filter period to travel time<br> 8 Distance in km<br> 9 Azimuth from the source to the receiver<br> 10 Spectral amplitude. <br> 11 Epicenter latitude <br> 12 Epicenter longitude<br> 13 Station latitude<br> 14 Station longitude<br> 15 control flag<br> 16 control flag<br> 17 Instantaneous period if this is preferred. This differs from the ilter period because the signal spectram is not flat.<br> 18 Comment: keyword<br> 19 Station <br> 20 Component<br> 21 Year<br> 22 Day of year<br> 23 Hour<br> 24 Minute - these identify the event origin time </p>
The datasets of line structured light scanning image of aggregate surface.
<p>We provided a dataset of linear structured light scanning road aggregates, with a total of 1267 images and labels of 7 aggregates, which can be used to train the semantic segmentation network.</p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Validation and testing parts
<p>The validation and testing parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</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,http://doi.org/10.1016/j.isprsjprs.2020.06.021.</p> <p>[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, http://doi.org/10.3390/rs13010157.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, doi: 10.1109/JSTARS.2022.3211857.</p>
Deuterium Metabolic Imaging repeatability dataset, Aarhus 2023
<p>### BACKGROUND</p> <p>Manuscript title: ‘Repeatability of deuterium metabolic imaging of healthy volunteers at 3T’<br> Authors: Nikolaj Bøgh, Michael Vaeggemose, Rolf Schulte, Esben SS Hansen, Christoffer Laustsen<br> Contact: Nikolaj Bøgh at nikolaj.boegh@clin.au.dk</p> <p><br> ### DESCRIPTION OF DATASET</p> <p>This dataset contains images and spectra of healthy volunteers imaged with deuterium metabolic imaging. The data files are structured as subject (sub-xx) —> session (ses-xx) —> run (run-xx). Each subject (n=6) were imaged at two sessions 6 weeks apart.<br> Each session consisted of 4 runs:<br> ⁃ run-01 before 2H-glucose administration<br> ⁃ run-02 30 minutes after 2h-glucose<br> ⁃ run-03 75 minutes after 2h-glucose<br> ⁃ run-04 120 minutes after 2h-glucose</p> <p>At each run, a T1-weighted anatomical image was acquired. In this public dataset, a defaced version is provided. The anatomical images are accompanied by the DMI data, consisting of NIFTI-files of the fitted metabolite maps and a .-mat file with the raw MRSI data.</p>
Maize Root Domain Shift Image Datasets, Segmentation Models, and RhizoVision Explorer Settings
<p>This .zip file contains the following: 1) a .csv file used for RhizoVision Explorer settings, 2) root segmentation .pkl models developed using RootPainter, and 3) folders of maize root images collected in the field and greenhouse experiment that were used to train models and that were manually annotated to generate ground-truth datasets.</p> <p>For clarification, the segmentation model file named "000053_1679678936_V7_2021_tiled.pkl" is the growth stage-specific V7 field model. The segmentation model file named "000040_1679498247_R2_2021_tiled.pkl" is the growth stage-specific R2 field model. The segmentation model file named "000022_1679880442_V7plusR2_2021_tiled.pkl" is the fine-tuned V7+R2 field model. The segmentation model file named "000059_1687007135_V7R2_Tiled_Combined_2021.pkl" is the combined V7+R2 field model. The segmentation model file named "000068_1687361521_V7extendedR2_tiled_2021.pkl" is the extended V7+R2 field model. The segmentation model file named "000027_1681159536_GH_R2_tiled.pkl" the R2 greenhouse model. The segmentation model file named "000059_1680366870_GH_V9_tiled.pkl" is the V9 greenhouse model.</p>
Age-Related Macular Degeneration Benchmark Imaging Dataset (ABID)
ClinicalTrials.gov study NCT06924021. IPD Sharing: NO. Countries: 7. Publications: 0.
Imaging and multi-omics datasets converge to define different neural progenitor origins for ATRT-SHH subgroups [bulkRNAseq_ATRT_cellLines]
GEO Series GSE241733. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Validated Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. <br><br> The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B TA as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Validated Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. <br><br> The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B TA as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
Loess landslide dataset with 11,010 images and all labeled.
<p>The dataset was created by a group at Chengdu University of Technology, with a total of 11,010 image data, and all of them have been labeled. The study area is in the Loess Plateau of China, where landslide disasters are frequent, so we created this dataset for deep learning to detect loess landslides for research.</p>
ForTrunkSpecies - Image datasets of annotated RGB(-NIR) images for tree trunk types detection and segmentation at ground-level
<p>Two annotated image datasets for detection and segmentation of two tree trunk species - eucalyptus and pine:</p> <ul> <li>ForSpeciesDet(_4channels): dataset for tree trunk species detection in RGB(-NIR) images;</li> <li>ForSpeciesSeg(_4channels): dataset for tree trunk species segmentation in RGB(-NIR) images.</li> </ul>
ForClearingDet - Image dataset of annotated RGB images for object detection in forestry clearing operations
<p>Image dataset of 4 annotated forestry object classes (tree trunks, rocks, vegetation and humans) for object detection during forestry clearing operations. The imbalance of the object class "human" in this dataset was compensated for by the utilisation of the dataset available at <a href="https://www.kaggle.com/datasets/karthika95/pedestrian-detection">https://www.kaggle.com/datasets/karthika95/pedestrian-detection</a>.</p>
Dataset for the project "Novel tailored network-based rTMS treatments in Alzheimer's disease: an integrated multi-imaging approach" (GR-2016-02364718)
<p><strong><span>Specific Aim 1:</span></strong></p> <p><span>To test the efficacy of two network-based rTMS treatment protocols in reducing cognitive impairment. In the first protocol, the treatment will aim at stimulating the Default Mode Network (DMN) whereas the second protocol will target the Central Executive Network (CEN).</span></p> <p><strong><span>Specific Aim 2:</span></strong></p> <p><span>To investigate the neural mechanisms underlying response to treatment. We will investigate the modulations of brain reactivity, plasticity and connectivity induced by the treatments and how they correlate with the clinical response, thus defining quantitative markers of clinical improvement. To this aim, we will use an integrated multi-imaging approach, which will involve the use of innovative measures obtained with MRI and neurophysiological tools (TMS-EEG co-registration and theta burst stimulation - TBS protocols of rTMS).</span></p> <p><span>The present dataset contains the following folders:</span></p> <p><span>RawData_Aim1 [Unpublished Data]</span></p> <p><span><span>-<span> </span></span></span><span>Materials and Methods_RawData_Aim1</span></p> <p><span><span>-<span> </span></span></span><span>RawData_Aim1: a dataset (excel file) containing the following sheets:</span></p> <p><span><span>o<span> </span></span></span><span>Demographic data (sheet 1)</span></p> <p><span><span>o<span> </span></span></span><span>Cognitive data – primary outcome (sheet 2)</span></p> <p><span><span>o<span> </span></span></span><span>Cognitive data – secondary outcomes (sheet 3)</span></p> <p><span>RawData_Aim2 [Unpublished Data]</span></p> <p><span><span>-<span> </span></span></span><span>Materials and Methods_RawData_Aim2</span></p> <p><span><span>-<span> </span></span></span><span>RawData_Aim2 - MRI: a dataset (excel file) containing the following sheet:</span></p> <p><span><span>o<span> </span></span></span><span>Sample (col A), scanner (col B) and timepoint (col C)</span></p> <p><span><span>o<span> </span></span></span><span>Rs-fMRI: functional connectivity (FC) values (col D-G)</span></p> <p><span><span>o<span> </span></span></span><span>DTI: microstructural integrity values: FA (col H-K) and MD (col L-O)</span></p> <p><span><span>o<span> </span></span></span><span>ASL: cerebral blood flow (CBF) values (col P-S)</span></p> <p><span> </span><span><span><span> </span></span></span><span>RawData_Aim2 - TMS-EEG: a folder containing individual folders for each patient, each of which includes the following subfolders:</span></p> <p><span><span>o<span> </span></span></span><span>T0: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham).</span></p> <p><span><span>o<span> </span></span></span><span>T1: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham).</span></p> <p><span><span>o<span> </span></span></span><span>T2: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham).</span></p> <p><span> </span><span><span><span> </span></span></span><span>RawData_Aim2 - TBS: a folder containing individual folders for each patient, each of which includes the following subfolders:</span></p> <p><span><span>o<span> </span></span></span><span>T0: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS.</span></p> <p><span><span>o<span> </span></span></span><span>T1: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS.</span></p> <p><span><span>o<span> </span></span></span><span>T2: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS.</span></p> <p><span> </span><span>Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.</span></p>
lymph node ultrasound image dataset
Open the record for dataset details and reuse information.
Image dataset of phantoms used for regular tests of radiography apparatus
<p>Dataset of images used in a study called "Design and use of a custom phantom for regular tests of radiography apparatus: a feasibility study".</p> <p>The dataset consists of images gathered in a four weeks span of two types of phantoms - custom and comercial.</p>
Silkworm eggs dataset based on terahertz images
<p>Terahertz sequential images of silkworm eggs 8 days before hatching</p>
Crab and other Fossil Radiographs using neutron imaging Dataset
<p>Crab and other fossil radiographs </p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-MIST1 (plasma 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>
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.