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365 results for “Spatial modeling”
Dataset for the manuscript: "Three-dimensional species distribution modeling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs"
<p>Whether the three-dimensional (3D) structure of habitats influences and partition recruitment niches of corals is unknown. We developed a new method that combined Species Distribution Modeling and Structure from Motion to characterize and map the three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and octocorals. </p> <p>In this repository, we include 48 3D models of small areas of the reef (i.e., within ~ 0.25 m<sup>2</sup> quadrats) reconstructed with Structure-from-Motion, as well as the geospatial data used to characterize and map the realized recruitment niche for scleractinian corals and octocorals on Caribbean coral reefs. We conducted the study at two shallow, fringing reefs off the south shore of St. John, US Virgin Islands, named Grootpan and Europa Bays (18° 18.360’N, 64° 43.140’W, and 18° 19.016’N, 64° 43.798’W, respectively). Within each 0.25 m<sup>2</sup> quadrat, we counted and marked all recruits (octocorals ≤ 5 cm height, and scleractinians ≤ 4 cm wide).</p> <p><em>DATASET DESCRIPTIONS:</em></p> <ul> <li><strong>"Quadname_data.zip":</strong> In each of this folders we included all the data calculated within a quadrat: <ul> <li>ASCII files (.txt).</li> <li>The annotated dense point cloud (.las) for each quadrat.</li> <li>The quadrat 3D model texture (.jpg).</li> <li>The quadrat 3D polygon mesh (.ply).</li> <li>The quadrat 2.5D Digital Elevation Model (i.e., DEM; .tif).</li> <li>Shape files with recruits local coordinates within each quadrat (.dbf, .prj, .shp, .shx).</li> </ul> </li> <li><strong>"datawide.rds": </strong>This is the file needed to run the analyses performed in Martínez-Quintana et al., 2023. This file is obtained after processing all the raw data calculated within each quadrat. All code associated with the workflow used to obtain the datawide.rds file and run the analyses performed in Martínez-Quintana et al., 2023 is available at <a href="https://github.com/AdamWilsonLab/meshSDM">github.com/AdamWilsonLab/meshSDM</a>.</li> </ul> <p><strong>IMPORTANT NOTES: </strong></p> <ul> <li>Quadrat names starting with the letters “eu” indicate the data were collected at Europa Bay, whereas those starting with the letters “ec” indicate that data were collected at Grootpan Bay (commonly named East Cabritte).</li> <li>Each ASCII file (quadname_ASCII_subsampled_X.txt) contains the slope and roughness of the quadrat calculated on the point cloud at 5, 10, 20, and 100 mm scales, and the smooth point cloud used to calculate the topographic exposure index (TEI) described in Martínez-Quintana et al., 2023. Calculations were performed and ASCII files were created with CloudCompare.</li> <li>Each dense point cloud, mesh, texture, and DEM were calculated with Agisoft Metashape.</li> <li>Agisoft Metashape allows the user to classify and annotate groups of points in the dense point cloud. However, the list of classes provided by the software corresponds to the standard list used for terrestrial LiDAR data; these classes cannot be renamed within the software. Thus, for the present study, we coded the automatic semantic classifications available in Metashape as follows: <ul> <li>Ground = Calcareous rock.</li> <li>Building = Igneous rock.</li> <li>High noise = Sand.</li> <li>Low vegetation = Adult Scleractinian corals.</li> <li>Medium vegetation = Adult Octocoral base.</li> <li>High vegetation = Sponge.</li> <li>Water = Octocoral recruit (named also ocr).</li> <li>Road Surface = Scleractinian recruit (named also scr).</li> <li>Unclassified = created points but never classified (excluded from the analyses).</li> <li>Low Point = noise (unreliable points).</li> <li>Transmission tower and Rail = Points outside the quadrat and excluded from the analysis.</li> </ul> </li> </ul>
Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture
<p>Dataset including both performance of human observers and the neural architecture, related to the manuscript:</p> <p>Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture</p>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves
<p>Aims: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar tiNanohertz ming arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal. Methods: In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. Results: Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.</p>
Data from: Emergent spatial patterns can indicate upcoming regime shifts in a realistic model of coral community
<p class="western"><span>Increased stress on coastal ecosystems, such as coral reefs, seagrasses, kelp forests and other habitats can make them shift towards degraded, often algae-dominated or barren communities. This has already occurred in many places around the world, calling for new approaches to identify where such regime shifts may be triggered. Theoretical work predicts that the spatial structure of habitat-forming species should exhibit changes prior to regime shifts,</span><span><em> </em></span><span>such as an increase in spatial autocorrelation. However, extending this theory to marine systems requires theoretical models connecting field-supported ecological mechanisms to data and spatial patterns at relevant scales. To do so, we built a spatially-explicit model of sub-tropical coral communities based on experiments and long-term datasets from Rapa Nui (Easter Island, Chile), to test whether spatial indicators could signal upcoming regime shifts in coral communities. Spatial indicators anticipated degradation of coral communities following increases in frequency of bleaching events or coral mortality. However, they were generally unable to signal shifts that followed herbivore loss, a widespread and well-researched source of degradation, likely because herbivory, despite being critical for the maintenance of corals, had comparatively little effect on their self-organization. Informative trends were found both under equilibrium and non-equilibrium conditions, but were determined by the type of direct neighbor interactions between corals, which remain relatively poorly documented. These inconsistencies show that while this approach is promising, its application to marine systems will require detailed information about the type of stressor, and filling current gaps in our knowledge of interactions at play in coral communities. </span></p>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</p>
Data from: Mate choice strategies in a spatially-explicit model environment
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A modeling framework for quantifying spatial recruitment dynamics using abundance estimation and sibship analysis: code and simulation study output
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Habitat suitability modeling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill
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Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
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Using machine learning to model nontraditional spatial dependence in occupancy data
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Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
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Prioritizing road-kill mitigation areas: a spatially explicit national-scale model for an elusive carnivore
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Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models
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The role of spatial structure in multi-deme models of evolutionary rescue
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The construction of small-scale, quasi-mechanistic spatial models of insect energetics in habitat restoration: a case study of beetles in Western Australia
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Data from: Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework
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Computer code for a model describing the emergence of a long transient regular spatial pattern from interaction of competing aquatic macrophytes and a biocontrol agent
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Data for fitting spatial capture-recapture (SCR) models to estimate spatially explicit demographics of Mojave desert tortoises on a demography plot in California, USA
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Data from: Emergent spatial patterns can indicate upcoming regime shifts in a realistic model of coral community
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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.