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423
datasets available to search
ShareScore release 0.9.0
Dataset results
423 results for “spatial use”
In vitro Modeling of the Human Dopaminergic System using spatially arranged ventral Midbrain-Striatum-Cortex Assembloids [scRNAseq]
GEO Series GSE219245. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics [RNA-seq]
GEO Series GSE188844. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [Xenium]
GEO Series GSE243168. Homo sapiens. 3 samples. Type: Other.
Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics (additional files)
GEO Series GSE218537. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Cell-Type Profiling of the Sympathetic Nervous System Using Spatial Transcriptomics and Spatial Mapping of mRNA
GEO Series GSE230780. Gallus gallus. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
Single-cell single-molecule spatial transcriptomics using CosMx on colectomy specimens collected during Pouch surgery in IBD patients.
GEO Series GSE283625. Homo sapiens. 8 samples. Type: Other.
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.
Defining Cellular Diversity at the Swine Maternal-Fetal Interface Using Spatial Transcriptomics and Organoids [MFI017_Visium]
GEO Series GSE296975. Sus scrofa. 15 samples. Type: Expression profiling by high throughput sequencing; Other.
Dynamic control of metabolic zonation and liver repair by endothelial cell Wnt2 and Wnt9b revealed by single cell spatial transcriptomics using Molecular Cartography
GEO Series GSE199463. Mus musculus. 5 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.
Salivary gland transcriptomic analysis in the IL-14α transgenic mouse model of Sjögren’s disease using Visium V1 3' spatial gene expression assay
GEO Series GSE298921. Mus musculus. 2 samples. Type: Other.
Decoding Heterogeneous and Coordinated Tissue Architecture in Glioblastoma Using Spatial Transcriptomics
GEO Series GSE253080. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing; Other.
In vitro Modeling of the Human Dopaminergic System using spatially arranged ventral Midbrain-Striatum-Cortex Assembloids [Smartseq]
GEO Series GSE219246. Homo sapiens. 22 samples. Type: Expression 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.
Distinguishing Keratoacanthoma from Well-Differentiated Cutaneous Squamous Cell Carcinoma Using Single-cell Spatial Pathology
GEO Series GSE235556. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Defining Spatiotemporal Gene Modules in Liver Regeneration Using Analytical Dynamic Visual Spatial -Omics Representation (ADViSOR)
GEO Series GSE244211. Mus musculus. 13 samples. Type: Other.
Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed paraffin-embedded tumor samples [MERFISH]
GEO Series GSE299886. Homo sapiens; synthetic construct. 3 samples. Type: Other.
Source matrices used to obtain the trophic and spatial seed dispersal networks
<p>1. Trophic relationships have inherent spatial dimensions associated with the sites where species interactions, or their delayed effects, occur. Trophic networks among interacting species may thus be coupled with spatial networks linking species and habitats whereby animals connect patches across the landscape thanks to their high mobility. This trophic and spatial duality is especially inherent in processes like seed dispersal by animals, where frugivores consume fruit species and deposit seeds across habitats.</p> <p>2. We analysed the frugivore-plant interactions and seed deposition patterns of a diverse assemblage of frugivores in a heterogeneous landscape in order to determine whether the roles of frugivores in network topology are correlated across trophic and spatial networks of seed dispersal.</p> <p>3. We recorded fruit consumption and seed deposition by birds and mammals during two years in the Cantabrian Range (N Spain). We then constructed two networks of trophic (i.e. frugivore-plant) and spatial (i.e. frugivore-seed deposition habitat) interactions and estimated the contributions of each frugivore species to the network structure in terms of nestedness, modularity and complementary specialization. We tested whether the structural role of frugivore species was correlated across the trophic and spatial networks, and evaluated the influence of each frugivore abundance and body mass in that relationship.</p> <p>4. Both the trophic and the spatial networks were modular and specialized. Trophic modules matched medium-sized birds with fleshy-fruited trees, and small bird and mammals with small-fruit trees and shrubs. Spatial modules associated birds with woody canopies, and mammals with open habitats. Frugivore species maintained their structural role across the trophic and spatial networks of seed dispersal, even after accounting for frugivore abundance and body mass.</p> <p>5. The modularity found in our system points to complementarity between birds and mammals in the seed dispersal process, a fact that may trigger landscape-scale secondary succession. Our results open up the possibility of predicting the consumption pattern of a diverse frugivore community, and its ecological consequences, from the uneven distribution of fleshy-fruit resources in the landscape.</p>
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks
<p>Abstract</p> <p>Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users.</p> <p>A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale.</p> <p>The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network.</p> <p> </p> <p>Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China)</p> <p> </p> <p>Citations</p> <p>If you find our code to be useful, please cite the following papers:</p> <p>Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020).</p> <p>Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021).</p> <p>Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).</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.