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26 results for “process model analysis”
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer's disease
<p>We introduce and study a new model for the progression of Alzheimer's disease incorporating the interactions of Aβ-monomers, oligomers, microglial cells and interleukins with neurons through different mechanisms such as protein polymerization, inflammation processes and neural stress reactions. In order to understand the complete interactions between these elements, we study a spatially-homogeneous simplified model that allows to determine the effect of key parameters such as degradation rates in the asymptotic behavior of the system and the stability of equilibriums. We observe that inflammation appears to be a crucial factor in the initiation and progression of Alzheimer's disease through a phenomenon of hysteresis, which means that there exists a critical threshold of initial concentration of interleukins that determines if the disease persists or not in the long term. These results give perspectives on possible anti-inflammatory treatments that could be applied to mitigate the progression of Alzheimer's disease. We also present numerical simulations that allow to observe the effect of initial inflammation and concentration of monomers in our model.</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part II: adjoint frequency response analysis, stochastic models, and synthesis
<p>Meta data updated after publication.</p> <p> </p>
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part I: statistical model and analysis of observational data
<p>Meta data updated after publication.</p> <p> </p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
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Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
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Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer’s disease
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Data from: Point process models for presence-only analysis
1. Presence-only data are widely used for species distribution modelling, and point process regression models are a exible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper we review point process models, some of their advantages, and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to): clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudoabsences or \background points") objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presenceonly species distribution modelling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software.
Superalloys fracture process inference based on overlap analysis of 3D models
<h2>Datasets and code utilized in the paper "Superalloys fracture process inference based on overlap analysis of 3D models"</h2> <h2>Code and data description</h2> <h3>Data for 3D reconstruction</h3> <ul> <li>Original SEM images of Fracture A - Fracture D obtained through the collection method in the paper.</li> </ul> <h3>Data for scale calibration</h3> <ul> <li>Original SEM images sequences of marked points 'dot1' and 'dot2' of Fracture A - Fracture D.</li> </ul> <h3>Sharpness score calculation</h3> <ul> <li>'shapeness.m' : Calculating image sharpness using normalized variance equations.</li> <li>'focus_A_data' - 'focus_D_data' : Sharpness scores for all images in the image sequences and their corresponding sample stage coordinates.</li> </ul> <h3>3D fracture models</h3> <ul> <li>Scale calibrated 3D models of Fracture A - Fracture D.</li> </ul> <h3>Description of internal cracks</h3> <ul> <li>Original images and EDS results for an illustration of the regions of internal crack generation in Fracture A</li> </ul> <p> </p> <p> </p>
Model Data and Diagnostics used for the Lake Victoria Process Analysis
<p>Model data and derived diagnostics used in the Lake Victoria analysis, from Unified Model output. © Crown Copyright, Met Office</p>
Data and analysis for "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"
<p>This contains all of the necessary data and code to reproduce the results of the manuscript submitted to the Journal of</p> <p>Hydrometeorology entitled "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"</p>
Processed data used for spatial analysis of DMD mouse models
<p>This repository contains seurat objects and .H5AD files that were used in the analysis described in the paper titled <strong>"Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models"</strong> Authors: L.G.M. Heezen, T. Abdelaal, M. van Putten, A. Aartsma-Rus, A. Mahfouz and P. Spitali</p> <p>It contains datafiles obtained from spatial transcriptomics (Visium, 10x Genomics) experiments on skeletal muscle samples from two wildtypes: C57BL10 and DBA/2J and two DMD mouse models: mdx and D2-mdx. All ten weeks old male mice, 10micron thick sections of the quadriceps.</p>
Data from: Regional paleoclimates and local consequences: Integrating GIS analysis of diachronic settlement patterns and process-based agroecosystem modeling of potential agricultural productivity in Provence (France)
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Data from: Point process models for presence-only analysis
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Supporting information, WRF output, and processed WRF files used for the generation of the manuscript "Observational and modelling analysis of Canada's only F5/EF5 tornado"
<p>WRF simulation output and processed files used to generate the figures and calculations in the manuscript "Observational and modelling analysis of Canada's only F5/EF5 tornado". Two zipped folders are attached, one is from the original control simulation with the microphysics scheme on (MP), and the other is from the experimental simulation with the microphysics scheme turned off (NOMP).</p><p>Each simulation zipped folder includes sub-directories containing the processed observed and simulated surface station data, surface wet-bulb potential temperature, cross sections (the MP simulation only), and convective parameter fields at 2100 UTC 22 June 2007.</p><p>A supplemental material in the form of a movie showing the radar observation between 2000 UTC 22 June 2007 and 0000 UTC 23 June 2007 is also attached. See the manuscript's Figure 5 caption for more information on the data shown in the animation.</p>
Data from: Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis
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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.