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127 results for “spatiotemporal dynamic”
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma
<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv - Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad - AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz - Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip - Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p>
Data from: Spatiotemporal dynamics of Nektonic biodiversity and vegetation shifts during the Smithian–Spathian Transition: Conodont and Palynomorph insights from Svalbard
<p>The dataset includes an Excel file with sporomorph counting of samples from the Stensiöfjellet section, Svalbard. Furthermore, it includes additional taxonomic notes on conodonts from the Stensiöfjellet section, Svalbard.</p>
Spatiotemporal dynamics of grassland aboveground biomass in northern China and the alpine region: Impacts of climate change and human activities
<p>We employed CASA model to estimate grassland Net Primary Productivity and aboveground biomass A(AGB) from meteorological and GIMMS Normalized Difference Vegetation Index (NDVI) remote sensing data in northern China. We analyzed the dynamics of grassland AGB and impacts climate change and human activites.</p>
Spatiotemporal dynamics of grassland aboveground biomass in northern China and the alpine region: Impacts of climate change and human activities
<p>We employed CASA model to estimate grassland Net Primary Productivity and aboveground biomass A(AGB) from meteorological and GIMMS Normalized Difference Vegetation Index (NDVI) remote sensing data in northern China. We analyzed the dynamics of grassland AGB and impacts climate change and human activites.</p>
Data from:Modelling the spatiotemporal dynamics of soil nitrogen in croplands of Northeast China from 1980 to 2023 using multisource data and machine learning
<p>This dataset include the spatiotemporal distribution and uncertainty of cropland soil total nitrogen content at 0-30, 30-60, 60-100 cm depths in Northeast China from 1980 to 2023. The long-time series of TN were estimated by using an space-time automatic machine learning. The detail information on the products were given below:</p> <p>Period: 1980-2023</p> <p>Spatial resolution: 0.004166667 degree (~500 m)</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Data format: GeoTIFF</p>
S3GM: Learning spatiotemporal dynamics with a pretrained generative model
<h1>Datasets of Kuramoto-Sivashinsky equation (KSE) and Kolmogorov flow.</h1> <h2>Description of KSE data:</h2> <p>Each file for KSE datasets contains 4 dimensions in the following order: (B*V)*T*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>V</td> <td>number of varying parameters</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 1 for KSE)</td> </tr> <tr> <td>values of parameter used to generate <strong>training </strong>dataset</td> <td>1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0</td> </tr> <tr> <td>values of parameter used to generate <strong>test </strong>dataset</td> <td>1.1, 2.5, 3.2</td> </tr> </tbody> </table> <h2>Description of Kolmogorov flow data:</h2> <p>Each file for Kolmogorov flow contains 5 dimensions inthe following order: (B*Re*K)*T*X*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>Re</td> <td>number of varying Reynolds numbers</td> </tr> <tr> <td>K</td> <td>number of varying source terms (controled by the value of k)</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 2 for Kolmogorov flow)</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>training </strong>dataset</td> <td>100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>test </strong>dataset</td> <td> <div> <div>50, 125, 575, 1100, 1500</div> </div> </td> </tr> <tr> <td>values of k used to generate <strong>training </strong>dataset</td> <td>2, 3, 4, 5, 6, 7, 8</td> </tr> <tr> <td>values of k used to generate <strong>test </strong>dataset</td> <td> <div> <div>2, 4, 6, 8</div> </div> </td> </tr> </tbody> </table> <h2>Description of ERA5 data:</h2> <p>Training and testing dataset for ERA5 contains 5 dimensions inthe following order: 1*T*X*X*C, which is manually collected from <a href="https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download">https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download</a>. <strong>Note that the quantities in the datasets are already rescaled</strong> (the scale factors are saved in the scalar_era5.npy file, which is a 4x2 array recording the means and stds for the 4 quantities we used). Details are listed in the following table:</p> <table> <tbody> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 4 for ERA5)</td> </tr> <tr> <td>time span for <strong>training </strong>dataset</td> <td>1979-2022</td> </tr> <tr> <td>time span for <strong>test </strong>dataset</td> <td> <div> <div>2023</div> </div> </td> </tr> </tbody> </table> <h2>Pretrained checkpoints:</h2> <p>The .zip file contains the pretrained checkpoints for KSE, Kolmogorov flow and ERA5. Within the .zip file, the folder'kse_v0' is the checkpoint for KSE, 'kol_v0' is the checkpoint for Kolmogorov flow, and 'era5_v0' is the checkpoint for ERA5.</p> <h1><em>Source code:</em></h1> <p>The source code is upload as Github repository in <a href="https://github.com/lzy12301/S3GM">https://github.com/lzy12301/S3GM</a></p>
100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales
<p>Image Datasets to 100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales</p>
Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
<p>Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km<sup>2</sup> survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km<sup>2</sup> grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.</p>
Data on: Climate drives the spatiotemporal dynamics of scrub typhus in China
<p>This dataset is the data used in the paper of Global change biology entitled "Climate drives the spatiotemporal dynamics of scrub typhus in China". We incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). This dataset provides important information on the projected cases of scrub typhus in mainland China under various RCPs (RCP4.5, RCP6.0, and RCP8.5) for three future time periods, which can help public health authorities refine their prevention and control measures to reduce the disease risk.</p>
Depth dependent spatiotemporal dynamics of overwintering pelagic Microcystis in a temperate water body
<p>Supplementary Information for the research article 'Depth dependent spatiotemporal dynamics of overwintering pelagic Microcystis in a temperate water body'.</p>
Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes
<p>Primates acquire gut microbiota from conspecifics through direct social contact and shared environmental exposures. Host behavior is a prominent force in structuring gut microbial communities, yet the extent to which group or individual-level forces shape the long-term dynamics of gut microbiota is poorly understood. We investigated the effects of three aspects of host sociality (social groupings, dyadic interactions, and individual dispersal between groups) on gut microbiome composition and plasticity in 58 wild Verreaux's sifaka (<i>Propithecus verreauxi</i>) from six social groups. Over the course of three dry seasons in a five-year period, the six social groups maintained distinct gut microbial signatures, with the taxonomic composition of individual communities changing in tandem among co-residing group members. Samples collected from group members during each season were more similar than samples collected from single individuals across different years. In addition, new immigrants and individuals with less stable social ties exhibited elevated rates of microbiome turnover across seasons. Our results suggest that permanent social groupings shape the changing composition of commensal and mutualistic gut microbial communities and thus may be important drivers of health and resilience in wild primate populations.</p>
Exploring spatiotemporal dynamics of flower visitor association pattern on two Avicennia mangroves: A network approach
<p>All the data sets used in the analyses of this study are provided here along with the <strong>R-Script.</strong><br> The datasets for foraging behaviour and Generalized linear mixed models will be provided upon request to the first or corresponding author of this article.</p> <p><strong>Please note that, in this R-script, we have shown the codes only for one dataset of respective analyses.</strong><br> <strong>PLEASE NOTE: In this R-script, there are two minor mistakes, as follows:<br> 1) Line no. 46<br> Present code: </strong><strong>nulls <- nullmodel(I_S.network, N=1000, method=3) ##(3=Vaznull) ## file name mistake<br> Correct code: nulls <- nullmodel(AO_Site, N=1000, method=3) ##(3=Vaznull)</strong></p> <p><strong>2) Line no. 55<br> Present code: AO_Site_V<-AM_Site[,-1] ##Omitting individual coloumn(species) ## file name mistake<br> Correct code: AO_Site_V<-AO_Site[,-1] ##Omitting individual coloumn(species)</strong></p> <ul> <li><strong>Description of the data set</strong></li> </ul> <p>Data explorers the plant-flower visitor network with spatiotemporal approaches. Here, AM denotes <em>Avicennia marina </em>and AO denotes <em>Avicennia officinalis. </em>For the overall site-visitor network (combining all years and all time frames) datasets are AO_Site and AM_Site.</p> <p>For the overall visiting time-visitor network (combining all years and all sites) the datasets are AO_Time and AM_Time</p> <p>For the site-visitor networks on the yearly scale, the datasets are AO_2016, AO_2017, AO_2018, AM_2016, AM_2017 and AM_2018.</p> <p>For the site-specific visiting time-visitor networks the datasets are AO_Satjelia, AO_Bali, AO_Sagar, AO_Bakkhali, AM_Satjelia, AM_Bali, AM_Sagar and AM_Bakkhali.</p>
Data and code for analyzing the spatiotemporal dynamics of laurel wilt in the southeastern US
<p>Data and code required to reproduce findings and figures from analyses of the spatiotemporal dynamics of laurel wilt in the southeastern United States.</p>
Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China
<p>This dataset is the data used in the paper of Global change biology entitled "<span>Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China</span>". <span>We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. </span><span>These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas. </span></p>
Land use and ecosystem service value spatiotemporal dynamics, topographic gradient effect and their driving factors in typical alpine ecosystems of the east Qinghai-Tibet Plateau: Implications for conservation and development
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Data from: Spatiotemporal dynamics of the ant community in a dry forest differ by vertical strata but not by successional stage
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Data from: Spatiotemporal dynamics of Nektonic biodiversity and vegetation shifts during the Smithian–Spathian Transition: Conodont and Palynomorph insights from Svalbard
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Spatiotemporal population dynamics of the Caddo Madtom (Noturus taylori), a narrow-range endemic of the Ouachita Highlands
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Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
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Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes
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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.