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75 results for “spatiotemporal model”
Data from: Spatiotemporal diversification of the true frogs (Genus Rana): a historical framework for a widely studied group of model organisms
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Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we employ genomic data (ddRADSeq) to test biologically-informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross-backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species, (b) distributional shifts resulted from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles, and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally-explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation (ABC) framework supported spatial configuration of topographic barriers to dispersal and human-driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model-based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.
A GDM-GTWR Coupled Model for Spatiotemporal Heteroge-neity Quantification of CO2 Emissions: A case of the Yangtze River Delta Urban Agglomeration from 2000 to 2017
<p>The compressed package contains some pictures and data on the paper.</p>
Figure 7 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 7. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/ km2 in log scale are mapped, S522c1 (A), S522c2 (B).
Figure S3 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S3. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/km2 in log scale are mapped, S782c1 (A), S782c2 (B), S1043sc1 (C), S1043sc2 (D), S1043sc3 (E).
Figure 6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 6. – Absolute values of spatial-temporal hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values in percentage (red). The light grey numbers represent the edge number of the tree.
Figure 1. – Eastern English Channel spatial grid using a in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 1. – Eastern English Channel spatial grid using a triangular mesh at a 522 km2 (A), 782 km2 (B) and 1043 km2 (C) average scale with the geographic coordinates in WGS84 of all the English Channel groundfish hauls survey from 1995 to 2014 (blue). The red points are the vertices used to define the mesh.
Figure 10 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 10. – Alosa sp. from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure 4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 4. – Spatial hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed in percentage (red). The light grey numbers represent the edge number of the tree.
Figure 9 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 9. – Eastern English Channel absolute spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities, AST522c1 (A), AST522c2 (B).
Figure 8 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 8. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST522c1 (A), ST522c2 (B).
Figure S6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S6. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST782c1 (A), ST782c2 (B), ST1043c1 (C), ST1043c2 (D).
Figure S1 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S1. – Spatial correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
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Spatiotemporal transcriptomic map of glial cell response in a mouse model of acute brain ischemia [spatial transcriptomics]
GEO Series GSE233814. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing; Other.
Spatiotemporal transcriptomic map of glial cell response in a mouse model of acute brain ischemia [scRNA-Seq]
GEO Series GSE233812. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Spatiotemporal characterization of the cellular and molecular contributors to liver fibrosis in a murine hepatotoxic-injury model
GEO Series GSE74605. Mus musculus. 13 samples. Type: Expression profiling by array.
Spatiotemporal transcriptomic map of glial cell response in a mouse model of acute brain ischemia [bulk RNA-Seq]
GEO Series GSE233811. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1
GEO Series GSE216137. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Integrated Spatial Transcriptomics and Single-Cell RNA Sequencing Reveal Lars2-Mediated Spatiotemporal Dynamics of Myocardial Remodeling in a Mouse Model of Transverse Aortic Constriction (TAC)
GEO Series GSE308859. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
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.