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470 results for “Spatial Patterns”
Data from: Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems
Describing, understanding and predicting the spatial distribution of genetic diversity is a central issue in biological sciences. In river landscapes, it is generally predicted that neutral genetic diversity should increase downstream, but there have been few attempts to test and validate this assumption across taxonomic groups. Moreover, it is still unclear what are the evolutionary processes that may generate this apparent spatial pattern of diversity. Here, we quantitatively synthesized published results from diverse taxa living in river ecosystems, and we performed a meta-analysis to show that a downstream increase in intraspecific genetic diversity (DIGD) actually constitutes a general spatial pattern of biodiversity that is repeatable across taxa. We further demonstrated that DIGD was stronger for strictly waterborne dispersing than for overland dispersing species. However, for a restricted data set focusing on fishes, there was no evidence that DIGD was related to particular species traits. We then searched for general processes underlying DIGD by simulating genetic data in dendritic-like river systems. Simulations revealed that the three processes we considered (downstream-biased dispersal, increase in habitat availability downstream and upstream-directed colonization) might generate DIGD. Using random forest models, we identified from simulations a set of highly informative summary statistics allowing discriminating among the processes causing DIGD. Finally, combining these discriminant statistics and approximate Bayesian computations on a set of twelve empirical case studies, we hypothesized that DIGD were most likely due to the interaction of two of these three processes and that contrary to expectation, they were not solely caused by downstream-biased dispersal.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Supplementary Tables S1 and S2 - Wild bird densities and landscape variables predict spatial patterns in HPAI outbreak risk across the Netherlands
<p>Table S1: Overview of candidate variables in order of (Rank) decreasing feature importance, including a selection of high risk bird species for HPAIV infection and land cover variables.</p> <p>Table S2: Aggregation of land cover classes to 5 major classes used in the random forest algorithms.</p>
Supporting data for ''Strong control of effective radiative forcing by the spatial pattern of absorbing aerosol"
<p>Supporting data for our Nature Climate Change study. We provide a subset of:</p> <p> - 2D, monthly fields</p> <p> - time-averaged 3D fields</p> <p> - a numpy/text file which is needed to identify the different aerosol plume experiments</p> <p> </p> <p>Please see the README for more information on the outputs, accessing specific plume experiments, and the data post-processing we conducted.</p>
Metabarcoding reveals seasonal and spatial patterns of arthropod community assemblages in two contrasting habitats
<p>Illumina reads, representing partial COI barcode. </p> <p>Quality filtered FLS and SLS reads:<br> Samples from oasis<br> FLS reads <br> 150615_I270_FCC7K1NACXX_L4_RSZAKPI005096-37_1.fq.gz<br> 150615_I270_FCC7K1NACXX_L4_RSZAKPI005096-37_2.fq.gz<br> SLS reads<br> 150615_I270_FCC7K1NACXX_L4_RSZAXPI005095-40_1.fq.gz<br> 150615_I270_FCC7K1NACXX_L4_RSZAXPI005095-40_2.fq.gz</p> <p>Samples from desert:<br> FLS reads<br> 150615_I270_FCC7K1NACXX_L4_RSZAKPI005098-39_1.fq.gz<br> 150615_I270_FCC7K1NACXX_L4_RSZAKPI005098-39_2.fq.gz<br> SLS reads<br> 150615_I270_FCC7K1NACXX_L4_RSZAXPI005097-41_1.fq.gz<br> 150615_I270_FCC7K1NACXX_L4_RSZAXPI005097-41_2.fq.gz</p>
Stochastic dispersal shapes the spatial pattern of species richness in mountain landscapes
<p class="MsoNormal"><strong><span>Aim<a name="OLE_LINK3"></a>: </span></strong><span><span>Biogeographers have begun to address the problem of species distribution patterns in three-dimensional space. A key question is: What patterns of species richness would arise on the three-dimensional surface of a landscape under minimal biological assumptions? Recently, a theory called "Landscape Elevational Connectivity" (LEC) has been developed, which measures how topography and geomorphology drive biodiversity patterns. Here, we tested the predictive ability of LEC for spatial patterns of species richness for the first time.</span></span></p> <p class="MsoNormal"><span><strong><span>Location: </span></strong></span><span><span>The Tibetan Plateau.</span></span></p> <p class="MsoNormal"><span><strong><span>Methods:</span></strong></span><span><span> We </span><span>used the "stacked species distribution models" (S-SDMs) approach to</span></span><span><span> estimate the empirical spatial distribution pattern of bird species richness on the Tibetan Plateau based on online species occurrence data and expert maps, and we compared this estimated distribution with the predictions of LEC.</span></span></p> <p class="MsoNormal"><span><strong><span>Results: </span></strong></span><span><span>We found a high correlation between the LEC null model and observed bird species richness in the biodiversity hotspot on the southeast edge of the Tibetan Plateau (Spearman's correlation, <em>r</em><span>s</span> = 0.746, 95% CI: 0.744-0.748). On a wider scale, LEC was better correlated with species richness in regions higher net primary productivity than in regions with lower net primary productivity.</span></span></p> <p class="MsoNormal"><span><strong><span>Main conclusions:</span></strong></span><span><span> <a name="OLE_LINK31"></a>Our results suggest that the impact of stochastic processes on the spatial distribution pattern of species richness may have been routinely underestimated, especially in regions with rich resources and high species richness. We conclude that it would be fruitful to reconsider the contribution of deterministic factors to the distribution pattern of species richness, especially in mountain landscapes, by applying LEC as a null model.</span></span></p>
Data used for Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures
<p>This upload contains four distinct zipped files with several different datasets contained within used for the analysis in publication "Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures" by Gary-Bicas et al., 2022 Description for each dataset is below.</p> <p>- External data (Contains data used for thermophysical and morphological analysis).</p> <ul> <li>binary files NBmap2007.bin (Putzig and Mellon, 2007) and nmap2003.bin( Putzig et al., 2005) . These contain global Mars thermal inertia maps using the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) </li> <li>Comma separated files with terminations "...USGS.csv" these are files extracting data for the studies' regions from the Mars global USGS geologic map #3292 (Tanaka et al., 2014) </li> <li>Shape file for bedrock designations (bedrock.[shp,shx,prj,dbf]) created by Cowart et al., 2019 where they mapped locations with bedrock exposures on Mars. We also include comma separated value files of the same maps for locations studied in this analysis (files with termination "...bedrock.csv")</li> <li>Shape file included has the locations of craters identified in all study regions for analysis (craters.point.[shp,shx,prj,dbf] and craters.polygon.[shp,shx,prj,dbf]) paired with the comma separated value intracrat.csv</li> <li>Shape file with study locations for analysis (windo_modeling_locations_revised3.[shp,shx,prj,dbf])</li> </ul> <p>- MRAMS data files.zip</p> <ul> <li>Contains 11 simulated climate states for each of the ten study location in analysis as well as Jezero crater using the Mars Regional Atmospheric Modeling System (MRAMS, Rafkin and Michaels, 2019). For each simulated case there are 4 seasonal time steps equating to 44 simulated cases for each study region in total (484 files) see associated python software publication indicating ingestion and processing of MRAMS datasets</li> </ul> <p>- MRAMS output files.zip</p> <ul> <li>After ingesting the datasets in MARS data files.zip into a python algorithm (see associated software publication) values for Wind Erosion Potential were extracted from the datasets and weighted sums were conducted to get annual values (see manuscript publication and associated python software publication,"MRAMS Data Output.ipynb") data was output into comma separated values for ease of use</li> </ul> <p>-MRAMS elevation and slope files.zip</p> <ul> <li>MRAMS data from output files.zip was further ingested into other algorithms to extract elevation and terrain slope values (see associated python software publication, "MRAMS Data Output.ipynb") that were output into comma separated values for ease of use</li> </ul> <p> </p> <p> </p>
Figure 8 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 8 - Map showing all collecting records for Megalocraerus spp.
Figure 7 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 7 - Dorsal habitus. A Megalocraerus madrededios B Megalocraerus tiputini.
Figure 2 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 2 - Lectotype of Megalocraerus rubricatus. A Dorsal B Ventral C Lateral D Pygidial habitus.
Comprehensive Analysis of Spatial, Temporal and Molecular Patterns of Ribociclib Efficacy and Resistance in Advanced Breast Cancer Patients
ClinicalTrials.gov study NCT05452213. IPD Sharing: NO. Countries: 1. Publications: 0.
Spatially and temporally distinct patterns of expression for VPS10P domain receptors in human cerebral organoids.
GEO Series GSE233567. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Nova-ST: Nano-Patterned Ultra-Dense platform for spatial transcriptomics [Nova-ST]
GEO Series GSE256318. Mus musculus. 7 samples. Type: Other.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
Open the record for dataset details and reuse information.
Data from: Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems
Open the record for dataset details and reuse information.
Stochastic dispersal shapes the spatial pattern of species richness in mountain landscapes
Open the record for dataset details and reuse information.
Data from: Incorporating the geometry of dispersal and migration to understand spatial patterns of species distributions
Open the record for dataset details and reuse information.
Spatially organized progenitors in the zebrafish heart field determine cardiovascular patterning [MERFISH]
GEO Series GSE294469. synthetic construct; Danio rerio. 2 samples. Type: Other.
Spatially-patterned and functional kidney assembloids recapitulate progenitor self-assembly and enable high-fidelity in vivo disease modeling
GEO Series GSE264516. Mus musculus. 34 samples. Type: Expression profiling by high throughput sequencing.
Nanotopography guides morphology and spatial patterning of induced pluripotent stem cell colonies
GEO Series GSE84848. Homo sapiens. 12 samples. Type: Expression profiling by array.
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.