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105
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ShareScore release 0.9.0
Dataset results
105 results for “temporal networks”
A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2 [single-cell RNA-seq]
GEO Series GSE197858. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
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.
A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2
GEO Series GSE197859. Mus musculus. 33 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
<p>Dataset for ICLR 2021 submission "Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling".</p>
"Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks": Dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p> </p> <p>The associated codes are available in the Github repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></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 from: Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memo ry encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
Plasticity of Neonatal Neuronal Networks Temporal Theta Activity, the First Endogenous
ClinicalTrials.gov study NCT03677908. IPD Sharing: NO. Countries: 0. Publications: 0.
Network Connectivity and Temporal Processing in Adolescents Who Stutter
ClinicalTrials.gov study NCT05286151. IPD Sharing: NO. Countries: 1. Publications: 0.
Temporal Interference for Thalamocortical Activity and Network Modulation
ClinicalTrials.gov study NCT07219719. IPD Sharing: YES. Countries: 1. Publications: 0.
Based on the Spatio-temporal Coding Characteristics of Frontotemporal Network, This Paper Explores the Mechanism of Five-tone Speech Training in Reshaping Language Fluency Function.
ClinicalTrials.gov study NCT07329751. IPD Sharing: NO. Countries: 0. Publications: 0.
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.
A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2 [ChIP-seq]
GEO Series GSE197856. Mus musculus. 16 samples. Type: Genome binding/occupancy 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.
Data from: Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Open the record for dataset details and reuse information.
A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2 [bulk RNA-seq]
GEO Series GSE197857. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
DeepND - Spatio-temporal Brain Co-Expression Networks
<p>DeepND - Spatio-temporal Brain Co-Expression Networks</p>
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [3d]
GEO Series GSE203585. Gallus gallus. 14 samples. Type: Expression profiling by high throughput sequencing.
Temporal analysis of hippocampal gene co-expression networks in the hyperthermia model of febrile seizures
GEO Series GSE84289. Rattus norvegicus. 55 samples. Type: Expression profiling by array.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [control]
GEO Series GSE203619. Gallus gallus. 21 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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.