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1,868 results for “Spatial Data”
Data from: Factors that can affect the spatial positioning of large and small individuals in clusters of sit-and-wait predators
<p>Shadow competition, the interception of prey by sit-and-wait predators closest to the source of prey arrival, is prevalent in clusters of sit-and-wait predators. Peripheral positions in the cluster receive more prey and should thus be more frequently occupied. Models predicting spatial positioning in groups, however, usually ignore variability among group members. Here, I used a simulation model to determine conditions under which small and large sit-and-wait predators, which differ in their attack range, should differ in their spatial positions in the cluster. Small predators occupied peripheral positions more frequently than large predators at the simulation beginning, while the opposite held true as time advanced. Due to the large and small attack range of large and small predators respectively, small predators mistakenly relocated away from peripheral positions, while large predators did not relocate fast enough from inferior central positions. Any factor that moderated the frequent relocations of small predators or had the opposite effect on large predators assisted small or large predators respectively to reach the more profitable peripheral positions. Furthermore, any factor elevating shadow competition led to longer occupation of the periphery by large predators. This model may explain why sit-and-wait predators are not homogenously distributed in space according to size.</p>
Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.
Data from: Conservation versus livelihoods: spatial management of non-timber forest product harvests in a two-dimensional model
Areas of high biodiversity often coincide with communities living in extreme poverty. As a livelihood support, these communities often harvest wild products from the environment. But harvest activities can have negative impacts on fragile and globally important ecosystems. This paper examines trade-offs in ecological protection and community welfare from the harvest of wild products. With a novel model and empirical evidence, I show that management of harvest activity does not always resolve these trade-offs. In a model of continuous harvests in a two-dimensional landscape, managed harvest activity improves welfare, but is uniformly bad for other ecosystem services that are sensitive to the presence (as opposed to the intensity) of human activity. Empirical results from a unique dataset of mushroom harvesters in Yunnan, China suggest more experienced, poorer, and more vulnerable individuals tend to rely on more distant harvests. Thus, policies that limit the extent of forest travel, such as protected areas, may protect fragile ecosystems but can have a disproportionately negative effect on those most vulnerable.
Data from: Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape
Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.
Data from: Tracking climate change in a dispersal-limited species: reduced spatial and genetic connectivity in a montane salamander
Tropical montane taxa are often locally adapted to very specific climatic conditions, contributing to their lower dispersal potential across complex landscapes. Climate and landscape features in montane regions affect population genetic structure in predictable ways, yet few empirical studies quantify the effects of both factors in shaping genetic structure of montane-adapted taxa. Here, we considered temporal and spatial variability in climate to explain contemporary genetic differentiation between populations of the montane salamander, Pseudoeurycea leprosa. Specifically, we used ecological niche modelling (ENM) and measured spatial connectivity and gene flow (using both mtDNA and microsatellite markers) across extant populations of P. leprosa in the Trans-Mexican Volcanic Belt (TVB). Our results indicate significant spatial and genetic isolation among populations, but we cannot distinguish between isolation by distance over time or current landscape barriers as mechanisms shaping population genetic divergences. Combining ecological niche modelling, spatial connectivity analyses, and historical and contemporary genetic signatures from different classes of genetic markers allows for inference of historical evolutionary processes and predictions of the impacts future climate change will have on the genetic diversity of montane taxa with low dispersal rates. Pseudoeurycea leprosa is one montane species among many endemic to this region and thus is a case study for the continued persistence of spatially and genetically isolated populations in the highly biodiverse TVB of central Mexico.
Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing - Image and In SItu Sequencing data
<p>Image and ISS data for Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rim.</p><p>Image data consists of:</p><ul><li>Nucler stain (DAPI)</li><li>Masks for liver, rim and tumor regions</li><li>H&E images of parallel tissue sections</li></ul><p>Gene and cluster marker data is collected in the <i>markers.h5ad</i> file which can be read using AnnData (<a href="https://anndata.readthedocs.io/en/latest/">https://anndata.readthedocs.io/en/latest/)</a>. </p><p> </p>
Data and code for the article " Dissimilarity of vertebrate trophic interactions reveals spatial uniqueness but functional redundancy across Europe"
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Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"
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Pre-Processed Data Sets for Marine Spatial Planning of a Wave-Powered Aquaculture Farm in the Northeast U.S.
<p>These data sets are intended for marine spatial planning applications including the modeling of wave-powered aquaculture farms. They work in tandem with the Python model developed by the SEA Lab to evaluate potential sites for this development in the Northeastern U.S. The code for this model is available on GitHub at <a href=" https://github.com/symbiotic-engineering/aquaculture">https://github.com/symbiotic-engineering/aquaculture</a>, and details about the data and model are discussed in several related publications.</p>
Data for the paper "Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars"
<p>This file contains input data for iSALE simulations reported in the publication:</p><p><i>"</i>Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars<i> by Aleksandra Sokolowska, Nicolas Thomas, and Kai Wuennemann</i></p>
spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics
<p>Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.</p>
Data in support to the paper: Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at four agricultural sites in Northern Italy: insights from spatial variability and representativeness by Emamalizadeh et al. (2024)
<p>The study was conducted within the 21GRD08 SoMMet project. The SoMMet project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (funder name European Partnership on Metrology; funder ID 10.13039/100019599; grant no. 21GRD08 SoMMet).</p>
A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IMC data)
<p>This repository contains all <strong>raw imaging mass cytometry (IMC) 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>ZTMA174_raw.zip - Contains raw IMC data for ZTMA 174.</p> <p>ZTMA249_raw.zip - Contains raw IMC data for ZTMA 249.</p>
Projections of spatial distributions of suitable environmental conditions for key Baltic Sea zooplankton species - Data
<p>This repository contains seven occurrences dataset which represent the station where the species have been identified, ranging from 2000 to 2020. The environmental projections for the period 2010-2020, as well as future projection on two horizons: from 2040 to 2050 and from 2090 to 2100 on two different scenarios: SSP245 and SSP585. The occurrences have been exctracted from OBIS (https://obis.org) and the environmental projections from Bio-ORACLE (https://bio-oracle.org).</p> <p> </p> <p>Occurrences datasets: </p> <ul> <li><em>Temora longicornis</em></li> <li><em>Centropages hamatus</em></li> <li><em>Limnocalanus macrurus macrurus</em></li> <li><em>Evadne nordmanni</em></li> <li><em>Acartia tonsa</em></li> <li><em>Acartia longiremis</em></li> <li><em>Acartia bifilosa</em></li> </ul> <p> </p> <p>Projections:</p> <ul> <li>Projection Baseline 2010-2020</li> <li>Projection 2040-2050 SSP245</li> <li>Projection 2090-2100 SSP245</li> <li>Projection 2040-2050 SSP585</li> <li>Projection 2090-2100 SSP585</li> </ul> <p> </p> <p> </p> <p> </p>
Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning
<p>Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning by David Pätzel, Richard Nordsieck and Jörg Hähner.</p>
Popek, R., Roy, A., Mandal, M., et al. (2024) Enhancing Urban Sustainability: How Spatial and Height Var-iability of Roadside Plants Improves Pollution Capture for Greener Cities - DATA
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Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics (output data)
<p>This contains the output results of CalicoST (inferred CNAs and cancer clones), results of comparison methods, and CNAs inferred from WES data of 13 samples across four cancer types.</p> <p>In this updated version, we also included the simulated data and the results from CalicoST and other methods in CalicoST_simulation_deposit.zip. README contains the details of deposited files.</p>
The raw data of Spatial omics of glioma
<p>Our files include the raw data from single-cell RNA sequencing, spatial transcriptomics sequencing conducted in our study. The raw data of the paper "Deciphering radial glial stem-like cells based on spatial multi-omics guides safe therapy in glioma".</p>
Raw IMC files for Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.
<p>Raw MCD files for the Stanford cohort of oral squamous cell carcinoma patients in this publication:</p> <p>Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data (DOI:<span> <a href="https://doi.org/10.1016/j.isci.2023.108486" target="_blank" rel="noopener">10.1016/j.isci.2023.108486</a>).</span></p>
Data Artifact for "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching"
<p>This dataset contains the Ligra, PARSEC, GAP, and QMM traces used in our paper "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching", which is accepted by HPCA'25. These traces are provided as part of the AE proces.</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)
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.