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464 results for “model structure”
Fig. 3 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 3. − Maps of mean maximum plant size (calculated as average of maximum plant size of all species predicted as present within a grid cell). A. Grasses (Poaceae) (30-360 cm); B. Woody species (3-25 m). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
Fig. 2 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 2. − Maps of species richness. A. All plant species (2-211 spp.); B. Graminoids (0-50 spp.); C. Forbs (0-86 spp.); D. Woody species (0-52 spp.); E. Weedy species (0-48 spp.); F. Non-weedy species (0-140 spp.). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
Raw data for: A model for the formation and evolution of structure of initial loess deposits
<p>The dataset includes the monitoring results of volumetric water content and matric suction during wetting and drying processes of initial loess deposits. The data are used for Figure 3 in the manuscript “A model for the formation and evolution of structure of initial loess deposits” (submitted to Geophysical Research Letters).</p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
DataSet: Structural and optical properties of gold nanosponges revealed via 3D nano-reconstruction and phase-field models
<p>These are the main raw and processed data for the publication "Structural and optical properties of gold nanosponges revealed<br> via 3D nano-reconstruction and phasefield models".</p> <p>Abstract:<br> Nanoporous gold nanoparticles are subject of intensive research due to their unique morphology, which leads to electric field localizations generating a strongly nonlinear optical response, allowing a wide range of applications. However, accurate predictions of physical properties require detailed knowledge of the sponges’ chaotic nanometer-sized geometrical structures, posing a metrological challenge. Therefore, a main goal is to obtain computer models with equivalent structural and optical properties. To understand the sponges’ morphology, a procedure for their accurate three-dimensional reconstruction using focused ion beam tomography is presented. Next, a small number of morphological key parameters is derived that sufficiently characterize the complex topology. Additionally, a new simulation method for the computer-aided creation of finite-sized sponges with adjustable geometric properties is presented. It is shown that if certain morphological parameters are similar for computer-generated and experimental sponges, their optical response, including number and locations of field localizations, are also similar. Finally, the anisotropy of the experimental sponges is analyzed and an easy-to-use procedure to replicate arbitrary anisotropies in computer-generated sponges is presented.</p>
Prosthetic Sockets: Tensile Behavior of Vacuum Infiltrated Fused Deposition Modeling Sandwich Structure Composites
<p>The development of novel materials will enable a new generation of prosthetic devices to be built with additive manufacturing (AM). Vacuum infiltrated sandwich structure composites are a promising approach for building prosthetic sockets via AM. In this dataset, we test the tensile properties of 18 different composite material configurations using ASTM D638. These composites were manufactured using a custom vacuum infiltration method and had varying filament materials, infiltrated matrix materials, and print directions. Several material-matrix-print composites showed higher ultimate tensile strengths and reduced anisotropy compared to full-infill control samples. However, the mechanical properties of these composites were limited by a large degree of porosity due to the manufacturing method. Still, the results were promising enough to create a proof of concept prosthetic socket via the vacuum infiltration method. Future research should focus on reducing porosity defects and investigating additional material-matrix-print combinations.</p>
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
STEP CAD models of structural aerospace sheet metal parts
<p>This dataset consists of 26 STEP CAD models of aerospace sheet metal parts. The models were used to test a prototype (A) of an automated feature recognition method (B) for aerospace sheet metal part models. When models are used in a work, they can be credited by citing them directly or by citing whichever of the following papers that are relevant.</p> <p>A - A Prototype of an Automated Feature Recognition Algorithm for Aerospace Sheet Metal Parts</p> <p>B - Feature Recognition for Structural Aerospace Sheet Metal Parts</p>
Structure of MAPbI3 surface models in VASP format
<p>Structure of MAPbI<sub>3</sub> surface models in VASP format, with the slab thickness of 6 stoichiometric units, along (001) planes. These models were generated for the study published in reference 1.</p> <p>(1) Lodeiro, L.; Barría-Cáceres, F.; Jiménez, K.; Contreras, R.; Montero-Alejo, A. L.; Menéndez-Proupin, E. Methodological Issues in First-Principle Calculations of CH3NH3PbI3 Perovskite Surfaces: Quantum Confinement and Thermal Motion. <em>ACS Omega</em> <strong>2020</strong>, <em>5</em> (45), 29477–29491. https://doi.org/10.1021/acsomega.0c04420.</p>
Hydraulic scale model experiments on the two-dimensional run-up and overtopping of solitary waves at a vertical wall and a dam-like structure
<p>This dataset includes the experimental data, which were generated during the study on the run-up and overtopping of solitary waves at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
Know what you don't know: Embracing state uncertainty in disease-structured multistate models
<p>Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark-recapture and dynamic multistate occupancy models are examples of HMMs that are frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity in state assignment. However, state uncertainty is a pervasive feature of many ecological systems, and multievent models were developed to explicitly account for it.</p> <p>We developed a novel extended multievent mark-recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false-negative and false-positive state assignment errors are modeled at two levels of state assignment---the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as survival, and the pathogen detection processes. We provide code to simulate and analyze datasets with various underlying ecological processes and fit our model to a mark-recapture dataset of <em>Mixophyes fleayi</em> (Fleay's barred frog) infected with the amphibian chytrid fungus (<em>Batrachochytrium dendrobatidis</em>, <em>Bd</em>).</p> <p>In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting <em>Bd</em>, pathogen detection was highly dependent on infection intensity, and false-positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4--5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model.</p> <p>Our results highlight that incorporating state assignment errors improves inference on the ecological state process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modeling state uncertainty in a range of related models. --</p>
"Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data". Raw data and JANA refinement files.
<p><strong>Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data</strong><br> 3D ED data sets of 5 compounds and JANA refinement files of 12 compounds</p> <p><strong>Relevant tools</strong><strong>:</strong></p> <ul> <li>PETS2: data reduction and analysis of electron diffraction patterns <ul> <li>Download program and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Palatinus, L. <em>et al.</em> Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst. B</em><strong>75</strong>, 512–522 (2019). <a href="https://doi.org/10.1107/S2052520619007534">DOI: 10.1107/S2052520619007534</a></li> </ul> </li> <li>JANA2006: crystal structure model refinement program <ul> <li>Download program from <a href="http://jana.fzu.cz/">http://jana.fzu.cz/</a> and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Results here were obtained with JANA2006. We recommend using JANA2020.</li> <li>Petricek, V., Dusek, M. & Palatinus, L. Crystallographic Computing System JANA2006: General features. <em>Z. Kristallogr.</em> <strong>229</strong>, 345–352 (2014). <a href="https://doi.org/10.1515/zkri-2014-1737">DOI: 10.1515/zkri-2014-1737</a></li> </ul> </li> <li>DYNGO: Bloch wave program, calculates dynamical diffraction intensities and derivatives <ul> <li>Program automatically included in JANA2006/JANA2020</li> <li>Palatinus, L., Petříček, V. & Corrêa, C. A. Structure refinement using precession electron diffraction tomography and dynamical diffraction: theory and implementation. <em>Acta Cryst. A</em><strong>71</strong>, 235–244 (2015). <a href="https://doi.org/10.1107/S2053273315001266">DOI: 10.1107/S2053273315001266</a></li> </ul> </li> </ul> <p><strong>3D ED data sets:</strong></p> <p>STW_HPM-1 (RT) was measured on a JEOL JEM-2100-LaB6 and diffraction patterns were recorded with an ASI Timepix detector. Another sample of STW_HPM-1 was measured at a temperature of 100 K after cryotransfer with a Titan Krios (CETA-D detector). The other data sets were measured on an FEI Tecnai G2 20 (Olympus SIS Veleta, CCD). Each data set contains the raw diffraction patterns (*.tif) and the basic input files needed to reproduce the data reduction with PETS2 as used in the associated publication (*.pts2, *.celllist, *.cenloc). Step-by-step tutorials are provided for quartz and glycine (and selected steps for abiraterone acetate) at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a>.</p> <ul> <li>α-quartz, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>natrolite, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>cobalt aluminophosphate (CAP), static ED patterns recorded in 0.1° steps (3 data sets from 2 crystals)</li> <li>abiraterone acetate, stepwise continous-rotation (5 data sets from 5 crystals)</li> <li>STW_HPM-1, continuous-rotation (1 data set, room temperature)</li> <li>STW_HPM-1, continuous-rotation (1 data set, <em>T</em> = 100 K, cryotransfer)</li> </ul> <p><strong>JANA refinement and CIF files:</strong></p> <p>CIF (Crystallographic Information Framework) files include two data items. The first is related to the dynamical and the second to the kinematical refinement. Relevant parameters and statistics specific for dynamical refinement are found in the field _refine_special_details.</p> <p>JANA files are provided for the dynamical and kinematical refinement at the stage after the final refinement cycle together with the original input files generated by PETS2. For quartz and natrolite, relevant files for the refinements against precession-assisted 3D ED data are included. For abiraterone acetate and limaspermidine, relevant files for the absolute structure determination are included.</p> <ul> <li>α-quartz</li> <li>albite</li> <li>mordenite</li> <li>natrolite</li> <li>STW_HPM-1</li> <li>cobalt aluminophosphate (CAP)</li> <li>CAU-36</li> <li>α-glycine</li> <li>carbamazepine</li> <li>(+)-limaspermidine</li> <li>abiraterone acetate</li> <li>MBBF4</li> </ul> <p>For the kinematical refinements based on more than one data set, the self-written tool "CompInt" (unpublished) was used. The tool can be found in the file "tool_scalehkl_compint.zip". Input (*.hkl, *.compint) and output files (*.scalehkl) are provided in the respective folder with the JANA files.</p> <p>Raw data sources of other data sets relevant for the associated publication are given in the SI of the associated publication.</p>
Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference
<p>The current knowledge about how protein structures influence sequence evolution is rarely incorporated into substitution models adopted for phylogenetic inference, which are commonly based on independent with the same substitution process and ignore the known variation of the evolutionary rates across sites with different structural properties. In previous works, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a strong selective force. Here we present novel structurally constrained substitution models (Str-CPE) based on Julián Echave's model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) of the wild-type protein, and better predict the observed substitution rates. Next, we combine Str-CPE and Stab-CPE models to obtain structure and stability constrained substitution models (SSCPE) that fit the empirical MSAs even better. Importantly, these SSCPE models present a relevant improvement of the phylogenetic likelihood for all ten protein families that we analyzed with the program RAxML-NG. We implemented the SSCPE models in the program Prot evol, freely available at <a href="https://github.com/ugobas/Prot_evol">https://github.com/ugobas/Prot_evol</a>.</p>
Frontiers in Ecology and Evolution 01 frontiersin.org Why grazing and soil matter for dry grassland diversity: New insights from multigroup structural equation modeling of micro-patterns
<p>Grazing is recognized as a major process driving the composition of plant<br> communities in grasslands, mostly due to the heterogeneous removal of<br> plant species and soil compaction that results in a mosaic of small patches<br> called micro-patterns. To date, no study has investigated the differences in<br> composition and functioning among these micro-patterns in grasslands in<br> relation to grazing and soil environmental variables at the micro-local scale.<br> In this study, we ask (1) To what extent are micro-patterns different from each<br> other in terms of species composition, species richness, vegetation volume,<br> evenness, and functioning? and (2) based on multigroup structural equation<br> modeling, are those differences directly or indirectly driven by grazing and soil<br> characteristics? We focused on three micro-patterns of the Mediterranean dry<br> grassland of the Crau area, a protected area traditionally grazed in the South-<br> East of France. From 70 plant community relevés carried out in three micro-<br> patterns located in four sites with different soil and grazing characteristics,<br> we performed univariate, multivariate analyses and applied structural equation<br> modeling for the first time to this type of data. Our results show evidence<br> of clear differences among micro-pattern patches in terms of species<br> composition, vegetation volume, species richness, evenness, and functioning<br> at the micro-local scale. These differences are maintained not only by direct<br> and indirect effects of grazing but also by several soil variables such as fine<br> granulometry. Biological crusts appeared mostly driven by these soil variables,<br> whereas reference and edge communities are mostly the result of different<br> levels of grazing pressure revealing three distinct functioning specific to each<br> micro-pattern, all of them coexisting at the micro-local scale in the studied<br> Mediterranean dry grassland. This first overview of the multiple effects of<br> grazing and soil characteristics on communities in micro-patterns is discussed<br> within the scope of the conservation of dry grasslands plant diversity.</p>
Figure 7. World model construction and artificial intelligent structure running on the PDA
<p>In this section the AI part of the software is briefly introduced. There are three distinct<br> layers: AI Core, Role Engine and Behavior Engine. AI Core receives the computed field data from<br> World Map Modeling unit and determines the play state according to the ball, opponents and our<br> robots positions.<br> Considering the current game strategy, determination of the play state is done by fuzzy<br> decision-making to avoid undesirable and sudden changes of roles or behaviors.[3-4]</p>
Figure 2: Impedance by means of Bode-plot representation, symmetric (con- tinuous line) and the asymmetric (dashed line) tree.-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>Figure 2 shows the total impedance by means of its Bode plot, for the symmetric and the asymmetric tree, whereas the airway tubes are modelled by an R ¡ L ¡ C element in both representations.<br> It is signi¯cant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-proximately ¡50o, resulting in a fractional order of n »=0:5. This observation suggests that a combined e®ect of more than one fractal order is present in the lungs and that it leads naturally to values closer to measured data in the low<br> frequency range.</p>
Figure 4: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case against averaged data from healthy subjects-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>It is significant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the<br> asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-<br> proximately ¡50o, resulting in a fractional order of n »=</p> <p>This observation suggests that a combined efect of more than one fractal order is present in the<br> lungs and that it leads naturally to values closer to measured data in the low<br> frequency range. In other words, the symmetric tree representation does not<br> suffice to obtain a good fit between the model and the measured impedance<br> data. Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4</p>
Figure 3: Number of branches for each generation, in the asymmetric (TOP) and symmetric (BOTTOM) generation. Notice that the Y-axis is logarithmic.-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>Figure 3 shows the number of branches that are in one generation, for the symmetric and asymmetric<br> lung structure. Notice the diferent slope which characterizes the space-filling distribution.</p>
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.