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115 results for “density modeling”
Revisiting the Density Profile of the Fuzzy Sphere Model for Microgel Colloids v2
<p>This Zenodo repository contains data supporting the paper “Revisiting the density profile of the fuzzy sphere model for microgel colloids”, Frank Scheffold, Soft Matter 2024, DOI: 10.1039/d4sm01045k </p>
2D visco-elasto-plastic subduction models in the presence of a high density and viscosity continental block
<p>Three main models with different rheological conditions and density are incorporated for an imposed intrusion between the upper and lower continental crust. Output files for a model with a crust without intrusions are also incorporated.</p> <p>The file contains data on the conditions of plate motion velocity, plate age, plate thickness in Excel (Input_Data_Age_Vel.xlsx), a table with the rheology used to reproduce different numerical models incorporating the intrusion (Table_S3.pdf), and a file High_density_materials.m showing the construction of the intrusion within the continental crust.</p> <p>High_density_materials.m m should be imposed on the original I2ELVIS code provided by Taras Gerya - ETH Zürich, Institut für Geophysik, Switzerland - email: taras.gerya@erdw.ethz.ch</p> <p>The file has the main time steps as a function of temperature, viscosity, density and rock composition into the folders. If you want to visualize them please run the following Script PLOT_OUTPUT_DATA.m where,</p> <p>str=string(2040), represent the time_step</p> <p>Files are organized in a matrix form, each folder has the</p> <p>grid_x :coordinate matrix, <br>gridy_: coordinate matrix, </p> <p>matrix_temperature_, density and viscosity.</p> <p>To plot the Rock composition.</p> <p>mx and my, tracer coordinates in x-direction and y-direction<br>matrix_markcom is matrix tracers.</p> <p> </p> <p>Model 1</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1620 = 3 Ma, 2040 = 0 Ma)</p> <p> </p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 1. <br>This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. <br>The high density material between the upper and lower crust in the vicinity of the slab is not included in Model 1. <br>The model evolves over a period of the last 15 Myr and shows a shallow subduction. </p> <p><br>Model 2</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 380= 12 Ma, 850 = 9 Ma, 1490 = 6 Ma, 2090 = 3 Ma, 2540 = 0 Ma)</p> <p>With high density block<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 2. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density material between the upper and lower crust in the vicinity of the slab is included in Model 2. The model evolves over a period of the last 15 Myr and shows a steep subduction.</p> <p><br>Model 3</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1690 = 3 Ma, 2040 = 0 Ma)</p> <p>Without high density block and high viscosity</p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 3. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The initial high viscosity material and without high density between the upper and lower crust in the vicinity of the slab is included in Model 3. The model evolves over a period of the last 15 Myr and shows subduction with a high dip angle. </p> <p><br>Model4</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 330= 12 Ma, 770 = 9 Ma, 1190 = 6 Ma, 1640 = 3 Ma, 2040 = 0 Ma)<br> <br>With high density block and high viscosity<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 4. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density and high viscosity material between the upper and lower crust in the vicinity of the slab is included in Model 4. The model evolves over a period of the last 15 Myr and shows a steep subduction</p>
Single-density photoionization models are not complex enough to describe AGN
<p>Nebulae photoionized by Active Galactic Nuclei are complex structures, exhibiting layers with different physical properties. Therefore, it is not possible to describe such objects using photoionization models of a single density, as shown in this work by the comparison of models in the BPT diagnostic diagrams.</p>
Parameters for smooth exponential atmosphere density model based on Jacchia-77
<p>The data sets provided here can be used to derive the <a href="https://doi.org/10.1016/j.asr.2019.03.016">smooth exponential atmosphere density</a> profile, fitted to the Jacchia-77 atmosphere density model. Both the static (at <span class="math-tex">\(T_\infty = 750, 1000, 1250K\)</span>) and variable model (for <span class="math-tex">\(T_\infty \in [650, 1350] K\)</span>) parameters are available.</p> <p>The model was derived to increase the accuracy of semi-analytically propagated orbits, in particular highly eccentric ones.</p>
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
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Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations
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Camera-trap data for fitting the random encounter model to estimate densities of coyotes and black-tailed jackrabbits in the Mojave Desert
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Data from: Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity
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The temporal response of a glioma cell population to irradiation: modeling the effect of dose and cell density
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Data from: Joint spatial modeling of cluster size and density for a heavily hunted primate persisting in a heterogeneous landscape
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Data from: A theoretical model for host-controlled regulation of symbiont density
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Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"
<p>Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low condition individuals that lacked the resources required for dispersal. Our model therefore generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.</p>
Data for "Bayesian inference of mantle viscosity from whole-mantle density models"
<p><strong>Supplementary Data</strong><br> Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems</p> <p>This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.</p>
Models to assess ability to achieve localized areas of reduced white-tailed deer density
<p>Localized management of white-tailed deer (<i>Odocoileus virginianus</i>) involves the removal of matriarchal family units with the intent to create areas of reduced deer density. However, application of this approach has not always been successful, possibly because of female dispersal and high deer densities. We developed a spatially explicit, agent-based model to investigate the intensity of deer removal required to locally reduce deer density depending on the surrounding deer density, dispersal behavior, and size and shape of the area of localized reduction. Application of this model is illustrated using the example of abundant deer populations in Pennsylvania, USA. Most scenarios required at least 5 years before substantial deer density reductions occurred. Our model indicated that a localized reduction was successful for scenarios in which the surrounding deer density was lowest (30 deer/mi²), localized antlerless harvest rates were <i>≥</i> 30%, and the removal area was 5 mi² or larger. When the size of the removal area was < 5 mi<sup>2</sup>, end population density was highly variable and, in some scenarios, exceeded the initial density. The shape of the area of localized reduction had less influence on the ability to reduce deer density than the size. There were no differences in mean deer density in the same size circle or square removal areas. Similarly, increasing the ratio of sides (length : width) in rectangular removal areas had little influence on the ability to locally reduce deer densities. Situations in which deer density was higher (40 or 50 deer/mi<sup>2</sup>) required antlerless removal rates to exceed 30% and took more than 5 years to considerably reduce density in the localized area regardless of its size. These results indicate that the size of the area of reduction, surrounding deer density, and antlerless harvest rate are the most influential factors in locally reducing deer density. Therefore, localized management likely can be an effective strategy for lower density herds, especially in larger removal areas. For high density herds, the success of this strategy would depend most on the ability of resource managers to achieve consistently high antlerless harvest rates.</p>
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"
This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.
Modelled 1-km resolution population densities for 49 bird species in the UK between 2007 and 2009
<p>This is a dataset derived from the BTO/JNCC/RSPB Breeding Bird Survey (BBS) used to produce <a href="https://www.bto.org/our-science/projects/breeding-bird-survey/latest-results/maps-population-density-and-trends">modelled maps of population density</a> for 49 bird species at 1-km square resolution across the UK between 2007 and 2009. </p> <p>Acknowledgement: These data may be used subject to the appropriate acknowledgement of the BTO/JNCC/RSPB Breeding Bird Survey. Acknowledgements should be in the form of the most recent BBS Report citation, available <a href="https://www.bto.org/our-work/science/publications/reports/bbs-reports" target="_blank" rel="noopener">online</a>, or as follows: “<em>The BTO/JNCC/RSPB Breeding Bird Survey is a partnership jointly funded by the BTO, RSPB and JNCC, with fieldwork conducted by volunteers.</em>”</p> <p>Please refer to the metadata for a more detailed description, and for information on dataset usage.</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Structural insights into distinct mechanisms of RNA polymerase II and III recruitment to snRNA promoters - segmented EM density used in integrative modeling
<p>segmented cryoEM map and derived gaussian mixture model used in the integrative modeling of the human SNAPc complex </p>
Experimental modelling of tsunamis generated by pyroclastic density currents: the effects of particle size distribution on wave generation
<p><strong>Data set for experimental videos modelling the entrance of a fluidised granular flow into the water.</strong></p> <p>The data set includes:</p> <ul> <li>a MATLAB script to calculated theoretical volume and density of a non-fluidised mixture.</li> <li>Properties of the fluidised granular flows on the fluidising ramp: volume, density, thickness, flow and impact Froude numbers, kinetic energy of the fluidised flows.</li> <li>Properties of the surface elevation: surface elevation at x = 1.6m away from the shoreline, velocity of the leading wave, maximum/minimum amplitudes, potential energy of the generated waves and the energy transfer between the flow and the water column.</li> <li>Properties of the underwater gravity current - displacement, velocity and thickness of the underwater velocity current.</li> <li>a MATLAB script to visualise the surface elevation.</li> </ul>
Figure 3 in Density estimations of the Asiatic black bear: application of the random encounter model
Figure 3. Monthly variation in the distance moved as calculated from 1-hour positioning with global positioning system (GPS) collars and estimated via GLMM.
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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.