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1,868 results for “Spatial Data”
Data for "The Spatial Evolution of Upward Positive Stepped Leaders Initiated from a 356-m-High Tower in Southern China"
<p>Here we presented the high-speed video, B-field data and E-field data of two cases (Case 1 & 2) analyzed in a manuscript entitled "The Spatial Evolution of Upward Positive Stepped Leaders Initiated from a 356-m-High Tower in Southern China". The video files can be opened by a software named as 'Phantom Camera Control Application', while the B-field & E-field data can be opende by Matlab. The data supports the aforementioned manuscript and can be used freely for scientific purposes after a request is made to author Mingli Chen.</p>
data and code for "Nest entrances, spatial fidelity, and foraging patterns in the red ant Myrmica rubra: a field and theoretical study"
<p>Data set and codes used for the results, figures and simulation of the paper "Nest entrances, spatial fidelity, and foraging patterns in the red ant <em>Myrmica rubra</em>: a field and theoretical study".</p>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
Data Sets for: Trace elements in aerosol from Northwest Pacific marginal sea, Indian Ocean and South Pacific to Antarctica: Spatial variability and source identification
<p>This dataset includes the concentrations of trace elements in aerosols, along with location and time information, collected during a cruise from November 2021 to April 2022. The cruise covered the Pacific, the Indian Ocean, the Southern Ocean.</p>
Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation
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Proteomics data for "Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section"
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A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IF/mIF data)
<p>This repository contains all <strong>raw whole-slide immunofluorescence (IF) data</strong> for the breast cancer study from Meyer et al., 2025. The code that was used to process and analyze the data is available at <a href="https://github.com/BodenmillerGroup/TNBC_publication">https://github.com/BodenmillerGroup/TNBC_publication</a>. </p> <p><strong>Structure:</strong><br>BasoLum.zip - Contains whole-slide IF data for CK5, CK7, CK19 stainings of selected TNBC patients (related to Figure 4)</p> <p><strong>NOTE:</strong> Raw <strong>multiplexed whole-slide immunofluorescence (mIF) data</strong> (related to Figure 5) for this study is available from the corresponding author upon reasonable request and has not been uploaded to Zenodo due to the large data size (~ 50 GB per image). </p>
Data for "Differential effects of Daphnia genotype composition on spatial environmental heterogeneity in experimental metacommunities"
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Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data for Speedy component resolution using spatially encoded diffusion NMR data
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The Data For Spatial Variations of Stellar Elemental Abundances in FIRE Simulations of Milky Way-Mass Galaxies: Patterns Today Mostly Reflect Those at Formation
<p>Spatial patterns of stellar elemental abundances encode rich information about a galaxy’s formation<br>history. We analyze the radial, vertical, and azimuthal variations of metals in stars, both today and at<br>formation, in the FIRE-2 cosmological simulations of Milky Way-mass galaxies, and we compare<br>with the Milky Way. Overall, spatial variations of stellar metallicities show only modest differences between formation and today; spatial variations today primarily reflect the conditions of stars at birth, with spatial redistribution of stars after birth contributing secondarily. </p> <p> </p> <p>This data abides by CC-BY.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and code for "Assessing the spatial scale of synchrony in forest tree population dynamics"
<p>The data sets and code provided here facilitate reproduction of our results from this paper on synchrony of forest tree population dynamics. </p> <h3>Description of the data and file structure</h3> <p>The analyses in the paper were conducted at three scales, and each involves its own data files:</p> <ul> <li>Local scale: The relevant data files are named, e.g., "BCI1-7,L=250m,dbh=100mm.Rdata", where "BCI1-7" indicates the ForestGEO site name ("BCI") and census intervals (1 to 7 for BCI), "L=250m" indicates the quadrat size, and "dbh=100mm" indicates the diameter-at-breast height (DBH) threshold used. There are 12 such files (two ForestGEO plots--BCI and Pasoh--times three quadrat sizes times two DBH thresholds). Each file contains a single list "N_all", whose length is equal to the number of quadrats at the given grain. Each element in the list is a data frame containing mean census times (in days), tree species' population sizes and number of survivors across the two censuses for the corresponding quadrat.</li> <li>Regional scale: The relevant data files are "Marena_data,dbh=100mm,spp_anonymised.Rdata" and "Marena_data,dbh=100mm,spp_anonymised.Rdata". Each file contains three objects: "dists" is a matrix giving the distances between all pairs of sites; "N_all1" is a list with one element for each plot, and each element being a data frame with (anonymised) species ids in the first column and abundances in the remaining columns (column names give mean census dates in days); "S_all1" has a similar structure to "N_all1" except that the data give numbers of survivors from any given census to any subsequent census (column headings indicate the two census numbers).</li> <li>Global scale: The relevant data files are "global_data,dbh=10mm,spp_anonymised.Rdata" and "global_data,dbh=100mm,spp_anonymised.Rdata". The data in the files have the same structure as in the regional-scale files.</li> </ul>
Data from: Using spatial capture–recapture to elucidate population processes and space-use in herpetological studies
The cryptic behavior and ecology of herpetofauna make estimating the impacts of environmental change on demography difficult; yet, the ability to measure demographic relationships is essential for elucidating mechanisms leading to the population declines reported for herpetofauna worldwide. Recently developed spatial capture–recapture (SCR) methods are well suited to standard herpetofauna monitoring approaches. Individually identifying animals and their locations allows accurate estimates of population densities and survival. Spatial capture–recapture methods also allow estimation of parameters describing space-use and movement, which generally are expensive or difficult to obtain using other methods. In this paper, we discuss the basic components of SCR models, the available software for conducting analyses, and the experimental designs based on common herpetological survey methods. We then apply SCR models to Red-backed Salamander (Plethodon cinereus), to determine differences in density, survival, dispersal, and space-use between adult male and female salamanders. By highlighting the capabilities of SCR, and its advantages compared to traditional methods, we hope to give herpetologists the resource they need to apply SCR in their own systems.
Data from: Non-equilibrium conditions explain spatial variability in genetic structuring of little penguin (Eudyptula minor)
Factors responsible for spatial structuring of population genetic variation are varied, and in many instances there may be no obvious explanations for genetic structuring observed, or those invoked may reflect spurious correlations. A study of little penguins (Eudyptula minor) in southeast Australia documented low spatial structuring of genetic variation with the exception of colonies at the western limit of sampling, and this distinction was attributed to an intervening oceanographic feature (Bonney Upwelling), differences in breeding phenology, or sea level change. Here, we conducted sampling across the entire Australian range, employing additional markers (12 microsatellites and mitochondrial DNA, 697 individuals, 17 colonies). The zone of elevated genetic structuring previously observed actually represents the eastern half of a genetic cline, within which structuring exists over much shorter spatial scales than elsewhere. Colonies separated by as little as 27 km in the zone are genetically distinguishable, while outside the zone, homogeneity cannot be rejected at scales of up to 1400 km. Given a lack of additional physical or environmental barriers to gene flow, the zone of elevated genetic structuring may reflect secondary contact of lineages (with or without selection against interbreeding), or recent colonization and expansion from this region. This study highlights the importance of sampling scale to reveal the cause of genetic structuring.
Data from: Functional traits and environmental conditions predict community isotopic niches and energy pathways across spatial scales
1. Despite ongoing research in food web ecology and functional biogeography, the links between food-web structure, functional traits and environmental conditions across spatial scales remain poorly understood. Trophic niches, defined as the amount of energy and elemental space occupied by species and food webs, may help bridge this divide. 2. Here, we ask how the functional traits of species, the environmental conditions of habitats and the spatial scale of analysis jointly determine the characteristics of trophic niches. We used isotopic niches as a proxy of trophic niches, and conducted analyses at spatial scales ranging from local food webs and metacommunities to geographically distant sites. 3. We sampled aquatic macroinvertebrates from 104 tank bromeliads distributed across five sites from Central to South America, and compiled the macroinvertebrates' functional traits and stable isotope values (δ15N and δ13C). We assessed how isotopic niches within each bromeliad were influenced by the functional trait composition of their associated invertebrates and environmental conditions (i.e., habitat size, canopy cover, and detrital concentration). We then evaluated whether the diet of dominant predators and, consequently, energy pathways within food webs, reflected functional and environmental changes among bromeliads across sites. Finally, we determined the extent to which the isotopic niches of macroinvertebrates within each bromeliad contributed to the metacommunity isotopic niches within each site, and compared these metacommunity-level niches over biogeographic scales. 4. At the bromeliad level, isotopic niches increased with the functional richness of species in the food web and the detrital concentration in the bromeliad. The diet of top predators tracked shifts in prey biomass along gradients of canopy cover and detrital concentration. Bromeliads that grew under heterogeneous canopy cover displayed less trophic redundancy and therefore combined to form larger metacommunity isotopic niches. Finally, the size of metacommunity niches depended on within-site heterogeneity in canopy cover. 5. Our results suggest that the trophic niches occupied by food webs can predictably scale from local food webs to metacommunities to biogeographic regions. This scaling process is determined by both the functional traits of species and heterogeneity in environmental conditions.
Data from: Spatial modeling improves understanding patterns of invasive species defoliation by a biocontrol herbivore
Spatial modeling has proven to be useful in understanding the drivers of plant populations in the field of ecology, but has yet to be applied to understanding variation in biocontrol impact. In this study, we employ multi-scale analysis (Moran's Eigenvector Maps) to better understand the variation in tree canopy exposed to defoliation by a biocontrol beetle (Diorhabda spp.). The control of the exotic tree Tamarix in riparian areas has long been a priority for land managers and ecologists in the American southwest. Diorhabda spp. was introduced as a bio-control agent beginning in 2001 and has since become an inseparable part of Tamarix-dominated river systems in the southwest. Between 2013 and 2016 tamarisk dieback was assessed at 79 sites across Grand County, Utah, arguably the epicenter of Diorhabda impact in the U.S. Canopy cover of Tamarix was between 73%-81% at these sites, with the percent that was live cover fluctuating by year with a minimum of 42%. Using a traditional general linear model, we found that readily and commonly measured environmental factors could explain only up to 26% of the variation in Tamarix live canopy each year, including that number of defoliations was correlated with an increase rather than a decrease in percent live canopy, suggesting compensatory growth. Spatial structure alone explained 22-40% of variation. We found fine scale spatial structure at less than 10 km and broad scale spatial structure from 10-30 km. Combining both traditional and novel spatial statistical methods we increased that percentage to 43-63%, depending on year. These results suggest that scientists and land managers must look beyond commonly measured environmental variables to explain non-random biocontrol impact in this system. In particular, this study points to the potential for biotic interactions and variation in flood cycles for further exploration of the identified spatial structure.
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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.