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147
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Dataset results
147 results for “Spatial Network”
Spatial Transcriptomics and Network-Based Bioinformatics Differentiate Intestinal Phenotypes of Cardiac and Classical Necrotizing Enterocolitis
GEO Series GSE268423. Homo sapiens. 56 samples. Type: Other.
EPAS1 directs a network of genes implicated in mitochondrial dysfunction in arrhythmogenic cardiomyopathy [Spatial Transcriptomics]
GEO Series GSE213540. Homo sapiens. 1 samples. Type: Other.
Temporal-spatial Establishment of Initial Niche for the Primary Spermatogonial Stem Cell formation is Determined by an ARID4B Regulatory Network
GEO Series GSE84808. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Temporal-spatial Establishment of Initial Niche for the Primary Spermatogonial Stem Cell formation is Determined by an ARID4B Regulatory Network (ChIP-Seq)
GEO Series GSE84802. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Spatial Coding Dysfunction & Network Instability in the Aging Medial Entorhinal Cortex
GEO Series GSE281777. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
The stem cell-type transcriptome of bioenergy sorghum reveals the spatial regulation of secondary cell wall networks
GEO Series GSE218642. Sorghum bicolor. 24 samples. Type: Expression profiling by high throughput sequencing.
Zika virus co-opts miRNA networks to persist in placental microenvironments detected by spatial transcriptomics [AGO-HITS-CLIP]
GEO Series GSE205609. Homo sapiens. 9 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types
GEO Series GSE73924. Homo sapiens. 3 samples. Type: Other.
A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis
GEO Series GSE73147. Homo sapiens. 24 samples. Type: Expression profiling by array.
Data for manuscript: Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks
<p>Data used to calibrate and run the SeRFE model in the applications presented in the manuscript "Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks."</p>
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>
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>
Data and Code accompanying "Dual communities in spatial networks"
<p>This data was generated in the publication "Dual communities in spatial networks" which is available in ArXiv:2105.06687.</p> <p>In addition to the data, a version of the code found in the <a href="https://github.com/phboett/dual-communities">Github repository</a> of this project was added.</p> <p>See README.md in repository for more information.</p>
Figure 2b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 2b "Network Edition" features. - Example of a road network uploaded into the application
Glioblastoma disrupts cortical network activity at multiple spatial and temporal scales
GEO Series GSE263832. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
The transcriptional networks governing the spatial regulation of RPE differentiation are regulated by the SWI/SNF complexes
GEO Series GSE210414. Mus musculus. 112 samples. Type: Expression profiling by high throughput sequencing.
Temporal-spatial Establishment of Initial Niche for the Primary Spermatogonial Stem Cell formation is Determined by an ARID4B Regulatory Network (RNA-Seq)
GEO Series GSE84804. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Source matrices used to obtain the trophic and spatial seed dispersal networks
Open the record for dataset details and reuse information.
Dual PAX3 and PAX7 transcriptional activities spatially encode spinal cell fates through distinct gene networks
GEO Series GSE288918. Mus musculus. 214 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Spatial gene regulatory network of miRNA165a in Shoot apical meristem of Arabidopsis
GEO Series GSE221139. Arabidopsis thaliana. 54 samples. Type: Expression profiling by high throughput sequencing.
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.