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23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “spatiotemporal clustering”
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'</p>
Figure S4 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 S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure S2 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 S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 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 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 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 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 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 S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
STGMVA: clustering, imputation, and integration for spatial resolved transcriptomics using spatiotemporal gaussian mixture variational autoencoder
<p> In this study, we present STGMVA, a comprehensive analysis toolkit employs a spatiotemporal gaussian mixture variational autoencoder to tackle these tasks effectively. STGMVA consists of two stages: pretraining the gene expression and spatial location using a gaussian mixture model, and learning the embedding vectors through a variational graph autoencoder. Results demonstrate STGMVA surpasses state-of-the-art approaches on various spatial transcriptomics datasets, exhibiting superior performance across different scales and resolutions. Notably, STGMVA achieves the highest clustering accuracy in human brain, mouse hippocampus, and mouse olfactory bulb tissues. Furthermore, STGMVA enhances and denoises gene expression patterns for gene imputation task. Additionally, STGMVA has the capability to correct batch effects and achieve joint analysis when integrating multiple tissue slices.</p>
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>Data for "Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants"</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).
Nup93 modulates spatiotemporal dynamics and function of the HOXA gene cluster during differentiation
GEO Series GSE130656. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Long-range genomic contacts and spatiotemporal chromatin landscape of human histone gene clusters at Histone Locus Bodies during the cell cycle in breast cancer [ChIP-seq]
GEO Series GSE229295. Homo sapiens. 46 samples. Type: Genome binding/occupancy 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.