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240
datasets available to search
ShareScore release 0.9.0
Dataset results
240 results for “High resolution maps”
Highly integrated single base resolution maps of the epigenome in Arabidopsis
GEO Series GSE10877. Arabidopsis thaliana. 16 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by genome tiling array; Methylation profiling by array; Methylation profiling by high throughput sequencing.
High-resolution mapping reveals a conserved, widespread, dynamic meiotically regulated mRNA methylation program [Hs]
GEO Series GSE55572. Homo sapiens. 50 samples. Type: Other.
High-resolution transcriptome mapping and N6-methyladenosine modification mapping of 3T3-L1 derived adipocytes (RNA-Seq)
GEO Series GSE171785. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
High-resolution mapping of regulatory element interactions and genome architecture using ARC-C [ChIP-Seq]
GEO Series GSE144671. Caenorhabditis elegans. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High resolution 3D chromatin maps of human primary monocytes [ChIP-Seq]
GEO Series GSE157970. Homo sapiens. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-resolution enzymatic mapping of genomic 5-hydroxymethylcytosine in mouse embryonic stem cells
GEO Series GSE42898. Mus musculus. 2 samples. Type: Methylation profiling by high throughput sequencing.
High resolution mapping of enhancer-promoter interactions [ChIA-PET]
GEO Series GSE62266. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer
GEO Series GSE290975. Mus musculus. 69 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide high resolution map of transcription start sites across the life cycle of Oikopleura dioica
GEO Series GSE78794. Oikopleura dioica. 6 samples. Type: Expression profiling by high throughput sequencing.
A simple method for generating high-resolution maps of genome wide protein binding
GEO Series GSE67454. Homo sapiens; Mus musculus; Drosophila melanogaster. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-resolution genome-wide mapping of AHR and ARNT binding sites by ChIP-Seq
GEO Series GSE41820. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-resolution mapping of regulatory element interactions and genome architecture using ARC-C [RNA-Seq]
GEO Series GSE144672. Caenorhabditis elegans. 4 samples. Type: Expression profiling by high throughput sequencing.
High-resolution Sox2 DNA-binding sites mapping by ChIP-exo in TSA-treated ES cells
GEO Series GSE62972. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
KSHV chromatin looping facilitates effective gene expression: high-resolution mapping of K-Rta binding sites in the KSHV genome [ChIP-seq]
GEO Series GSE99943. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-resolution map of copy number variations in motor cortex of Control and Sporadic Amyotrphic Lateral Sclerosis patients by using a customized exon-centric comparative genomic hybridization array.
GEO Series GSE107375. Homo sapiens. 40 samples. Type: Genome variation profiling by array.
Malaria in High-Resolution: Modelling and Mapping Plasmodium falciparum Parasite Rate using Very-High-Resolution Satellite Derived Indicators in Sub-Saharan African Cities
<p>Metadata, results and supplementary material of the following <a href="https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00232-2#Sec28">article</a>.</p> <p>The Out of Bag error of the Kampala Land use product is 18,57%.</p> <p>The Out of Bag error of the Dar es Salaam land use product is 16%.</p> <p>More information on the LC products can be found in <a href="https://zenodo.org/record/3711903#.YGGo0tLiuzU">here</a> and <a href="https://zenodo.org/record/3711905#.YGGtUNLiuzU">here</a>.</p> <p>This research is funded by the Belgian Science Policy through the <a href="http://react.ulb.be/">REACT</a> project.</p> <p> </p>
High-resolution global building height map (GBHM, 10 m) developed based on Sentinel imagery
<p>Due to the limited number of files on the Zenote platform, the tif files were grouped into the zip packages by longitude. In terms of file and zip package naming, lon represents longitude, lat represents latitude, A represents the east or north latitude, B represents west or south latitude, and the numbers after A and B represent the specific longitude or latitude in the lower left corner of the file.</p> <p>To compress the size of the data, the data is scaled by a factor of 100. Therefore, the pixel values need to be divided by 100 when used, i.e., a pixel value of 1500 represents 15 m.</p>
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset comprises processed outputs from an unmanned aerial vehicle (UAV) image acquisition campaign conducted across 27 study sites in Serbia. Each site is organized in a separate folder, labeled by study site name, and includes the following output data for both Object-Based Image Analysis (OBIA) and Convolutional Neural Network (CNN) approaches.</p> <table> <tbody> <tr> <td> <p><span>S.No</span></p> </td> <td> <p><span>Data Alias</span></p> </td> <td> <p><span>File Type</span></p> </td> <td> <p><span>Description</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>name_of_the_study site_multiband (OBIA)</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Five band raster orthomosaic containing RGB, DSM and NDVI layers rescaled from (0-255).</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Vectorized_r3 (OBIA+LSMS)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>The output from the segmentation process and is a basis of preparation for data labeling.</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>train_val_set (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Contain labeled samples of land use classes for training and validation sets for respective study site.</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>ClassifiedVector (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Classified vectorized output in rectangle shape.</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>ClassifiedVector_fixed (OBIA+RF)</span></p> </td> <td> <p><span>.shp</span></p> </td> <td> <p><span>Final output of the classified orthomosaic in a vector file containing all the classes in the attribute table.</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>RandomForest (OBIA)</span></p> </td> <td> <p><span>.txt</span></p> </td> <td> <p><span>Represents trained model.</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>confusion_matrix (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Confusion matrix for each study site</span></p> </td> </tr> <tr> <td> <p><span>8</span></p> </td> <td> <p><span>number_of_polygons (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Contains the number of polygons marked for training and validation.</span></p> </td> </tr> <tr> <td> <p><span>9</span></p> </td> <td> <p><span>class_area_percentage (OBIA+RF)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Refers to percentage coverage of each class for a given study site.</span></p> </td> </tr> <tr> <td> <p><span>10</span></p> </td> <td> <p><span>name_of_the_study_site_result (OBIA)</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Image showing the evaluation metric values for each site.</span></p> </td> </tr> <tr> <td> <p><span>11</span></p> </td> <td> <p><span>train_val_set_CNN</span></p> </td> <td> <p><span>.geojson</span></p> </td> <td> <p><span>Bounding box labeling dataset used for CNN model training for each site.</span></p> </td> </tr> <tr> <td> <p><span>12</span></p> </td> <td> <p><span>train_parameter (CNN)</span></p> </td> <td> <p><span>.csv</span></p> </td> <td> <p><span>Hyperparameters used for training the CNN model.</span></p> </td> </tr> <tr> <td> <p><span>13</span></p> </td> <td> <p><span>CNN_models</span></p> </td> <td> <p><span>.h5</span></p> </td> <td> <p><span>CNN models for each site trained with defined hyperparameters.</span></p> </td> </tr> <tr> <td> <p><span>14</span></p> </td> <td> <p><span>CNN_confusion_matrix</span></p> </td> <td> <p><span>.png</span></p> </td> <td> <p><span>Confusion matrix for each study site.</span></p> </td> </tr> <tr> <td> <p><span>15</span></p> </td> <td> <p><span>Classified_rasters_CNN</span></p> </td> <td> <p><span>.tif</span></p> </td> <td> <p><span>Classified rasters through CNN model, both reclassified and colormapped.</span></p> </td> </tr> </tbody> </table>
Detection of Amyloid Deposits in the Wrist by MRI With Mapping and High Resolution Sequences in Systemic Amyloidosis (AMYLOCARP)
ClinicalTrials.gov study NCT05150353. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High Resolution Gastric Mapping and Gastroduodenal Manometry
ClinicalTrials.gov study NCT06941545. IPD Sharing: NO. Countries: 1. Publications: 0.
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.