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450 results for “Spatio-Temporal”
Fig. 1 in Spatio-Temporal Structure And Reproductive Success In A Rook (Corvus Frugilegus) Colony
Fig. 1. Temporal distribution of nest building and egg laying. Bars represent the number of nests that were started to build (white) and number of nests in which the first egg was laid (grey) grouped into five-day periods. n = 20, mean±SD: 40.00±5.94 and 76.33±8.80 days, respectively
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
The spatio-temporal program of liver zonal regeneration
<p>We performed mouse bulk liver mRNA sequencing (mcSCRBseq, Bagnoli et al., 2018), single-cell RNA seq (Feature Barcode 10x) and spatial transcriptomics (10x Visium) of hepatocytes and non-parenchymal cells (NPCs) at several time points along the time course of Acetaminophen (APAP)-induced liver damage and regeneration to identify spatio-temporal dynamics of liver regeneration. </p> <p> </p>
Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]
<p>Supplementary Material of the PhD [Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]</p> <p> </p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Fig. 4 in Spatio-Temporal Structure And Reproductive Success In A Rook (Corvus Frugilegus) Colony
Fig. 4. Distance of the newly built nests from the centre of the colony, from the edge of the colony (metres) and number of neighbouring nests within 6 metres (medians, full circles) and those of the randomly selected points (medians, empty squares) at each sampling date. 100 random points were selected for each date, sample sizes of the observed nests are shown above the medians. In the marked (*) cases the values of the observed nests dif- fered significantly from the values of the ran- dom points (Mann-Whitney test, P <0.05)
Data and Code for "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach"
<p>This is a zipped file of all associated code and data for the submitted paper entitled "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach".</p>
Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)
<p>This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022</p>
Figure 1 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 1. Map of the RPPN Foz do Rio Aguapeí and location of the six studied areas in the RPPN Foz do Rio Aguapeí. Legend: (1) Lagoa São Gabriel; (2) Lagoa das Piranhas; (3) Lagoa dos Porcos; (4) Constructed wetland; (5) Aguapei river –; and (6) Lagoa da sede. Sources: CESP (2013) and Google Earth (2021).
Figure 2 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 2. Cumulative curve of the 52 waterfowl bird species in the RPPN Foz do Rio Aguapeí showing stability from sample 27 to 31.
Figure 3 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 3. NMDS (stress of 0.097) of the spatial distribution of the aquatic bird community recorded by the transect method in the lagoons of the RPPN Foz do Aguapeí, during the dry (rounded symbols) and rainy seasons (square symbols). Legend: LS = Lagoa da Sede; LSG = Lagoa São Gabriel; LP = Lagoa da Piranha and LPO = Lagoa dos Porcos.
Fig. 9 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 9: Histogram of the Mantel test assessing the relationship between genetic and morphologic distance for Gobius niger. Sim: simulations; Frequency: frequency values of the correlation between the genetic and morphologic distances. The dot represents the original value of the correlation between the distance matrices.
Fig. 6 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 6: PCA of the morphological variables of Gobius niger (standard length, SL; body height, BH; head length, HL; snout length, SnL; eye diameter, ED; first dorsal fin, DF1; second dorsal fin, DF2; anal fin, AF; pectoral fin, PF; ventral fin, VF) with projection of phenotypic groups. PC1 vs. PC2 and PC2 vs. PC3. The percentage of variation explained by each PC axis is given within parentheses.
Fig. 3 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 3: Cluster analysis associated with the similarity profile test (SIMPROF), based on abundances of Gobius niger, reveals reciprocal relations among the 20 sampled stations in the Marchica Lagoon using the Bray–Curtis distance.
Fig. 2 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 2: Picture of Gobius niger from the Marchica Lagoon showing the main measurements taken: total length (TL), standard length (SL), head length (LT), snout length (SnL), body height (BH), and eye diameter (ED).
Fig. 7 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 7: Linear regression of the principal component score axis (PC1) from morphometric measurements on the log standard length of Gobius niger with projection of phenotypic groups.
Fig. 8 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 8: Haplotype network constructed from 16S rDNA sequences of Gobius niger. The size of a particular circle reflects the haplotype frequency. The numbers indicate the nodes.
Fig. 1 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 1: Map showing the geographical localization of the Marchica Lagoon and the sampling stations of Gobius niger.
Fig. 4 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 4: Two-dimensional redundancy analysis (RDA) ordination representing the spatial distribution of Gobius niger related to the predictor variables selected through the best linear models based on distance (DISTLM). SM: suspended matter.
Figure 4 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 4. NMDS (stress of 0.001) of the spatial distribution of the aquatic bird community recorded by the transect method in the lotic environments of the RPPN Foz do Aguapeí, during the dry (rounded symbols) and rainy seasons (square symbols). Legend: AR = Aguapeí River and CW = Constructed wetland.
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.