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196
datasets available to search
ShareScore release 0.9.0
Dataset results
196 results for “Spatial map”
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [scRNA-seq and Visium]
GEO Series GSE243275. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Transcriptomic profiling of shed cells enables spatial mapping of cellular turnover in human organs
GEO Series GSE301268. Homo sapiens. 31 samples. Type: Expression profiling by high throughput sequencing.
Spatial Mapping of Mouse Brain Aging through Indexed Sequencing
GEO Series GSE270383. Mus musculus. 52 samples. Type: Expression profiling by high throughput sequencing; Other.
Mapping the immune cell microenvironment by digital spatial profiling in muscle tissue injected with the venom of Daboia russelii
GEO Series GSE222977. Mus musculus. 63 samples. Type: Other.
Spatial transcriptomics map of the embryonic mouse brain: a tool to explore neurogenesis
GEO Series GSE240715. Mus musculus. 4 samples. Type: Other.
High-Resolution Spatial Map of the Human Facial Sebaceous Gland Reveals Marker Genes and Decodes Sebocyte Differentiation [MERFISH]
GEO Series GSE292394. Homo sapiens; synthetic construct. 1 samples. Type: Other.
Spatial mapping of transcriptomic and lineage plasticity in metastatic pancreatic cancer [CosMx]
GEO Series GSE277782. Homo sapiens. 7 samples. Type: Other.
Cell-Type Profiling of the Sympathetic Nervous System Using Spatial Transcriptomics and Spatial Mapping of mRNA [Spatial Transcriptomics]
GEO Series GSE230778. Gallus gallus. 4 samples. Type: Other.
Mapping alterations spatially and temporally during early stages of breast tumourigenesis [aCGH]
GEO Series GSE72652. Homo sapiens. 36 samples. Type: Genome variation profiling by genome tiling array.
A Compendium of Chromatin Contact Maps Reveal Spatially Active Regions in the Human Genome
GEO Series GSE87112. Mus musculus; Homo sapiens. 19 samples. Type: Other; Expression profiling by high throughput sequencing; Third-party reanalysis.
Spatial and temporal mapping of breast cancer lung metastases identify TREM2 macrophages at the metastatic boundary
GEO Series GSE231915. Mus musculus. 148 samples. Type: Expression 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.
Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells
GEO Series GSE108561. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
Variable chromatin secondary structures in live cells revealed by radiation-induced spatially correlated DNA cleavage mapping [RICC-Seq]
GEO Series GSE81806. Homo sapiens. 29 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Cell-Type Profiling of the Sympathetic Nervous System Using Spatial Transcriptomics and Spatial Mapping of mRNA [RNA-seq]
GEO Series GSE230772. Gallus gallus. 1 samples. Type: Expression profiling by high throughput sequencing.
Spatial Proximity Sequencing Maps Developmental Dynamics in the Germinal Center
GEO Series GSE304749. Homo sapiens. 6 samples. Type: Other.
Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation
GEO Series GSE223066. Mus musculus. 34 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Mapping the Spatial Dynamics of the Human Oral Mucosa in Chronic Inflammatory Disease
GEO Series GSE206621. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m
<p>The expansion of planted forests often encroaches upon natural forests, leading to numerous environmental and social problems. Mapping natural and planted forests is crucial for the monitoring, management, and conservation of these invaluable forest resources. However, global mapping of natural and planted forests at fine spatial resolution remains an unaddressed need. Here, we generated more than 70 million training samples from dense Landsat images and fed them to a random forest classifier (RF). Our dataset achieved an impressive overall accuracy of 85% when validated against reference data. (Note: </p> <p>(1) The data for artificial and natural forests is displayed on a single map as an RGB image, where green pixels represent natural forests, yellow pixels indicate artificial forests, and other pixels correspond to non-forest areas.</p> <p>(2) There was an error during the upload of some tiles (specifically, tiles 300 to 400) in the first version. This portion of the data has been supplemented on the same data platform: <a href="https://doi.org/10.5281/zenodo.13759567" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.13759567</a>.</p> <p>(3) Our algorithm processes tiles that contain forested areas. Therefore, the nodata areas may correspond to regions with very small forest areas or no forest cover at all in tile.)</p>
Spatial scale evaluation of forecast flood inundation maps
<p>Spatial scale evaluation of forecast flood inundation maps, data and code</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for a new scale-selective approach to forecast flood map evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- JBA Consulting Flood Foresight forecast flood maps used in the study. </p> <p>Helen Hooker. (2022). Spatial scale evaluation of forecast flood inundation maps (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6011882</p> <p>Related publications:</p> <p>Spatial scale evaluation of forecast flood inundation maps; 2022; Journal of Hydrology (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</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.