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450 results for “Spatio-Temporal”
Supplementary material 1 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Tables S1–S11 : Explanation note: This is a DOC file with all the temporal information of the occurrence of the algae taxa in both localities, and all the information of the PERMANOVA analyzes used in this study.
Spatio-temporal Features of Intra-seasonal Oceanic Variability in the Philippine Sea from Mooring Observations and Numerical Simulations
<p>This dataset contains the NPOCE (http://npoce.org.cn) data used in the following submission for Journal of Geophysical Research: Oceans:</p> <p>Hu, S., J. Sprintall, C. Guan, B. Sun, F. Wang, G. Yang, F. Jia, J. Wang, D. Hu, and F. Chai (2018), Spatio-temporal Features of Intra-seasonal Oceanic Variability in the Philippine Sea from Mooring Observations and Numerical Simulations, Journal of Geophysical Research: Oceans.</p> <p>Variables in this dataset are eddy kinetic energy (EKE) observed by the NPOCE moorings, longitudes, latitudes, depths and dates.</p> <p> </p> <p> </p>
Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development
<p>This upload contains the expression prediction dataset discussed in the manuscript "Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development" and used by the webserver https://find.princeton.edu.</p> <p>Abstract:</p> <p>Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput <em>in situ</em> hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like <em>Drosophila</em>, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current <em>in situ</em> hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver <a href="http://find.princeton.edu/">http://find.princeton.edu</a>, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Data of Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction
<p>Using the dense ground-based GNSS receiver network and ionosonde data from East and Southeast Asia during 2010-2021, a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP) was developed. The model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities.<br>This dataset includes both the model and the results generated by STEP. The parameters provided are: UT (hours), Latitude (°), Longitude (°), Date, Y_pred (TECU/min), and Y_true (TECU/min). The dimensions of Y_pred and Y_true are 10812 x 610, where 10812 represents the product of the number of date and the number of UT (minus 18), and 610 corresponds to the product of the number of Latitude and Longitude. The model with a .pth extension can be loaded using PyTorch.</p>
Data from: Predicting the spatio-temporal dynamics of biological invasions: Have rapid responses in Europe limited the spread of the Asian yellow-legged hornet (Vespa velutina nigrithorax)?
<p>This data was collated from multiple sources including aktion-wespenschutz.de (Germany), The Biological Records Centre, UKCEH (UK), GBIF, L'Inventaire national du patrimoine naturel (France), iNaturalist (Belgium), StopVelutina (Italy), Waarneming.nl (Netherlands), MAGRAMA. Inventario Español del Patrimonio Natural y la Biodiversidad (Spain). </p> <p>Coordinates of occurence records were obatained from the sources above. These records were used in ecological niche models and mechanistic models to simulate the spread of <em>Vespa velutina nigrithorax</em> in Belgium, Germany, The Netherlands and the United Kingdom. </p> <p>Please be sure to cite and credit the orginal data providers if you intend to use this data. </p>
CoRL Submission Data/Results for Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization
<p>Dataset/Results for reproducibility/verification of results in CoRL 2021 submission "Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization"</p>
Data from: Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA
We assessed spatio-temporal patterns of diversity in deep-sea sediment communities using metabarcoding. We chose a recently developed eukaryotic marker based on the v7 region of the 18S rRNA gene. Our study was performed in a submarine canyon and its adjacent slope in the Northwestern Mediterranean Sea, sampled along a depth gradient at two different seasons. We found a total of 5,569 molecular operational taxonomic units (MOTUs), dominated by Metazoa, Alveolata and Rhizaria. Among metazoans, Nematoda, Arthropoda and Annelida were the most diverse. We found a marked heterogeneity at all scales, with important differences between layers of sediment and significant changes in community composition with zone (canyon vs slope), depth, and season. We compared the information obtained from metabarcoding DNA and RNA and found more total MOTUs and more MOTUs per sample with DNA (ca. 20% and 40% increase, respectively). Both datasets showed overall similar spatial trends, but most groups had higher MOTU richness with the DNA template, while others, such as nematodes, were more diverse in the RNA dataset. We provide metabarcoding protocols and guidelines for biomonitoring of these key communities in order to generate information applicable to management efforts.
Data from: Sixty years of anthropogenic pressure: a spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines
Analyses of historical samples can provide invaluable information on changes to the genetic composition of natural populations resulting from human activities. Here, we analyze 21 microsatellite loci in historical (archived scales from 1927-1956) and contemporary samples of brown trout (Salmo trutta) from six neighbouring rivers in Denmark, to compare the genetic structure of wild populations before and after population declines and stocking with non-local strains of hatchery trout. We show that all populations have been strongly affected by stocking, with admixture proportions ranging from 14 to 64%. Historical population genetic structure was characterized by isolation-by-distance and by positive correlations between historical effective population sizes and habitat area within river systems. Contemporary population genetic structure still showed isolation-by-distance, but also reflected differences among populations in hatchery trout admixture proportions. Despite significant changes to the genetic composition within populations over time, dispersal rates among populations were roughly similar before and after stocking. We also assessed whether population declines or introgression by hatchery strain trout should be the most significant conservation concern in this system. Based on theoretical considerations, we argue that population declines have had limited negative effects for the persistence of adaptive variation, but admixture with hatchery trout may have resulted in reduced local adaptation. Collectively, our study demonstrates the usefulness of analyzing historical samples for identifying the most important consequences of human activities on the genetic structure of wild populations.
Data from: Drivers of spatio-temporal patterns of salinity in Spanish rivers: a nationwide assessment
The salinization of freshwaters is a global water quality problem that leads to the biological degradation of aquatic ecosystems. However, little is known about the spatial extent of freshwater salinization and the relative contribution of each human activity (e.g. agriculture, urbanization, mining or shale-gas extraction). Here, we investigated environmental factors that explain spatio-temporal patterns of water salinity and examined the causes, the extent and the degree of salinization of Spanish rivers. Results showed a strong variation in water salinity among river typologies and between river reaches in good and poor ecological status according to the Water Framework Directive. The variation in water salinity was largely explained by a combination of natural (i.e. climate and geology) and anthropogenic (i.e. land use) factors. By contrast, land use factors as urbanization and agriculture were the main drivers of salinization, which affected more than one quarter of the rivers and streams in Spain, especially those in the most arid regions (central and southern regions) and in the main courses of the largest rivers such as the Ebro, Douro and Tajo rivers. The information provided here can be relevant to set priority regions and actions to ameliorate freshwater salinization.
Spatio-temporal dynamics of abiotic and biotic properties explain biodiversity-ecosystem functioning relationships
<p>There is increasing evidence that spatial and temporal dynamics of biodiversity and ecosystem functions play an essential role in biodiversity-ecosystem functioning (BEF) relationships. Despite the known importance of soil processes for forest ecosystems, belowground functions in response to tree diversity and spatio-temporal dynamics of ecological processes and conditions remain poorly described. We propose a novel conceptual framework integrating spatio-temporal dynamics in BEF relationships and hypothesized a positive tree species richness effect on soil ecosystem functions through the spatial and temporal stability of biotic and abiotic soil properties based on species complementarity and asynchrony. We tested this framework within a long-term tree diversity experiment in Central Germany by assessing soil ecosystem functions (soil microbial properties and litter decomposition) and abiotic variables (soil moisture and surface temperature) for two consecutive years in high spatial and temporal resolution. Tree species richness and identity had significant effects on soil properties (e.g., soil microbial biomass). Structural equation modeling revealed that overall soil microbial biomass was partly explained by (a) enhanced temporal stability of soil surface temperature and (b) decreased spatial stability of soil microbial biomass. Overall, spatial stability of soil microbial properties was positively correlated with their temporal stability. These results suggest that spatio-temporal dynamics are indeed crucial determinants in BEF relationships and highlight the importance of vegetation-induced microclimatic conditions for stable provisioning of soil ecosystem functions and services.</p>
COMPOSITIONAL SPATIO-TEMPORAL PM2.5 MODELLING IN WILDFIRES (R SCRIPT AND DATASET)
<p>The present R script and dataset were used in the assessment of a spatio-temporal PM<sub>2.5</sub> model in wildfire events with a limited number of monitoring stations using a compositional approach (CoDa). </p>
Data-driven gap filling and spatio-temporal filtering of the GRACE and GRACE-FO records
<p>Gravity Recovery And Climate Experiment and Follow On (GRACE/-FO) global monthly measurements of Earth's gravity field have led to significant advances in quantifying mass transfer. However, a significant temporal gap between missions hinders evaluating long-term mass variations. Moreover, instrumental and processing errors translate into large non-physical North-South stripes polluting geophysical signals. We use Multichannel Singular Spectrum Analysis (M-SSA) to overcome both issues by exploiting spatio-temporal information of Level-2 GRACE/-FO solutions, filtered using the DDK7 decorrelation and a new complementary filter, built based on the residual noise between fully processed data and a parametric fit to observations. Using an iterative M-SSA on Equivalent Water Height (EWH) time series processed by CSR, GFZ, GRAZ, and JPL, we replace missing data and outliers to obtain a combined evenly sampled solution. Then, we apply M-SSA to retrieve common signals between each EWH time series and its same-latitude neighbours to further reduce residual spatially uncorrelated noise. Comparing GRACE/-FO M-SSA solution with SLR and SWARM low-degree Earth’s gravity field and hydrological model demonstrates its ability to satisfyingly fill missing observations. Our solution achieves a noise level comparable to mass concentration (mascon) solutions over oceans (3.2 mm EWH), without requiring \textit{a priori} information nor regularisation. While short-wavelength signals are challenging to capture using highly filtered spherical harmonics or mascons solutions, we show that our technique efficiently recovers localized mass variations using well-documented mass transfers associated with reservoir impoundments.</p>
Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
<p>Composite data with NWM 2.0 streamflow, basin PET, drainage area and stoage.</p> <p>SAR data used in this study are downloaded from</p> <p><a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
A spatio-temporal dataset on food flows for four West African cities
<p>Gaining insight into the food sourcing practices of cities is important to understand their resilience to climate change, economic crisis, as well as pandemics affecting food supply and security. To fill existing knowledge gaps in this area food flow data were collected in West Africa for four cities - Bamako (Mali), Bamenda (Cameroon), Ouagadougou (Burkina Faso), and Tamale (Ghana). The data covers, depending on the city, road, rail, boat, and air traffic. Surveys were conducted for one week on average during the peak harvest, lean, and rainy seasons, resulting in a dataset of over 100,000 entries for 46 unprocessed food commodities. The data collected includes information on the key types of transportation used, and quantity, source, and destination of the food flows. The data were used to delineate urban foodsheds and to identify city-specific factors constraining rural-urban linkages. They can be used to inform academic and policy discussions on urban food system sustainability, to validate other datasets, and to plan humanitarian aid and food security interventions.</p> <p>Workflow and supplementary information associated with this dataset are found on GitHub (https://zenodo.org/record/7813686#.ZDQmKvbP23A). The data paper provides information on how data were collected and processed as well as how to use the data (https://www.nature.com/articles/s41597-023-02163-6): Karg, H., Akoto-Danso, E.K., Amprako, L. <em>et al.</em> A spatio-temporal dataset on food flows for four West African cities. <em>Sci Data</em> <strong>10</strong>, 263 (2023). https://doi.org/10.1038/s41597-023-02163-6</p> <p> </p> <p> </p>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p> <p>The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id.</p> <p>The simulations ids are divided as follows:</p> <p>- 1-80: Training and validation</p> <p>- 501-520: Testing dataset 1</p> <p>- 10001-10020: Testing dataset 2</p> <p>- 15001-15020: Testing dataset 3</p>
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Discordant spatio-temporal dynamics of functional and phylogenetic diversity of rotiferan communities exposed to aquaculture effluent
<p>The growth of the human population brought about the global intensification of aquacultural production, and aquaculture became the fastest growing animal husbandry sector. Effluent from aquaculture is an anthropogenic environmental burden, containing organic matter, nutrients, and suspended solids that affect water quality, especially in water bodies of high biodiversity and conservation value. Water quality assessment often relies on bioindicators, analysing changes in taxonomic diversity of various freshwater organismal groups. Stepping beyond taxon diversity, we used functional and phylogenetic diversities of rotifers to identify factors affecting their community organization in response to an aquaculture effluent gradient in the largest oxbow lake in the Carpathian Basin, Hungary. Sampling was carried out three times per season at five points along a 3.5 km section of the oxbow lake, including the point of effluent inflow. We used eight traits to evaluate functional diversity: body size, trophi type, feeding mode, protection type, body wall type, corona type, habitat preference, and tolerance level. Functional and phylogenetic distances among the 24 species identified indicated trait conservatism. Rotiferan diversity increased with increasing distance from the point of influx in spring and summer. Among the factors affecting community organization in spring and summer, we find examples of environmental filtering, while in autumn the role of biotic interaction is more frequent. Under nutrient-rich conditions in spring and summer, organisms belonging to the same functional group were dominant, while under oligotrophic conditions more diverse but less abundant groups were present. Considering functional and phylogenetic traits allowed us to identify organising forces of rotifer communities in the largest oxbow lake of the Hungarian Lowland.</p>
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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)
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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.