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147
datasets available to search
ShareScore release 0.9.0
Dataset results
147 results for “Spatial Network”
F I G U R E 2 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 2 Distribution of Tetrahymena population densities depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape, distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 2 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 1 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 1 Median population densities (in thousands of individuals) of Tetrahymena in corresponding dendritic (a) and linear (b) landscapes at the end of the experiment (day 15) and across the three replicate landscapes. In these landscapes, outer nodes are labelled "O," inner and central nodes are labelled "I" and "C", respectively. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 5 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 5 Euclidean distances moved by Tetrahymena individuals depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 3 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
(raw dataset) "Spatial biology of Ising-like synthetic genetic networks"
<p>This is the raw data for the manuscript Simpson et al "Spatial biology of Ising-like synthetic genetic<br> networks"</p>
Spatialized metabolomic annotation combining MALDI imaging and molecular network
<p>These data are linked to a publication "Spatialized metabolomic annotation combining MALDI imaging and molecular network" where we studied the in situ chemical diversity of fruits of the species Sextonia rubra (Mez.) Van der Werff (Lauraceae) using mass spectrometry imaging techniques and the annotation of molecular species detected by molecular networks using MetGem software. This repository contains: MS1 and MS2 raw data, ion mapping of the fruit according to different tissues, total molecular networks and a script for processing the acquired data to reproduce this approach.<br> <br> </p>
Data from: Finding the best management policy to eradicate invasive species from spatial ecological networks with simultaneous actions
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Data from: The contribution of land tenure diversity to the spatial resilience of protected area networks
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Data from: Evaluating the sensitivity of process domains for logjams to spatial and temporal sample size in river networks of the Southern Rockies, USA
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Data from: Network-scale effects of invasive species on spatially-structured amphibian populations
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Data from: Interaction networks of macrofungi and mycophagous beetles reflect diurnal variation and the size and spatial arrangement of resources
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The topology of spatial networks affects stability in experimental metacommunities
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Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk
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Data from: The influence of spatial sampling scales on ant-plant interaction network architecture
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Data from: Invariant antagonistic network structure despite high spatial and temporal turnover of interactions
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Spatial covariation of fish population vital rates in a stream network
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Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks
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Data from: Ecological divergence among colour morphs mediated by changes in spatial network structure associated with disturbance
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Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
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Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.
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.