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17 results for “Graphical Models”
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. Supplemental material.
<p>Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification, i.e., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged. This dataset provides Supplemental material for the PhysiCell Studio publication.</p>
Graphic representation of data set for the project "IRI model performance evaluation for the Mexican region"
<p>Here, we illustrate the modeling and experimental results for vertical Total Electron Content (TEC) over Mexico during the five year period 2018-2022. The results were obtained for the UCOE GNSS receiver station (geographic coordinates: 19.6°N; 101.68°W ). The calculations were made each two hours during the whole period under considerations. The modeling results were obtained using the "International Reference Ionosphere (IRI)" model, which is an empirical climatological model based on ground and space observations of the ionosphere [Bilitza et al., 2022].</p>
New Zealand native forest plant cover data for Popovic et al. MEE (2019), Untangling direct species associations from indirect mediator species effects with graphical models.
<p>Forest cover measurements were collected at 1246 native forest sites that form part of a network of permanent 20 x 20 m plots spread throughout New Zealand. A total of 1831 plant species were present in these plots, with the most common being herbs, graminoids, ferns, shrubs and trees. Plant cover (in ordinal categories) was assessed for each species in several tiers at different heights. The cover data we analysed (<em>NZ_native_forest_cover.csv) </em>were the maximum cover recorded over all the tiers at the 964 sites identified as native forests, containing 1311 species with at least one presence. <em>NZ_native_forest_species.csv</em> contains species data including species name, exotic/native, and plant type (tree, shrub, etc.), corresponding to the plant species in the columns of <em>NZ_native_forest_cover.csv</em>.</p> <p>We acknowledge the use of data drawn from the Natural Forest plot data collected between January 2002 and March 2007 by the LUCAS programme for the Ministry for the Environment, New Zealand.</p> <p> </p>
Data for analyses by graphical loglinear Rasch models of the PSFP and the PLCFP subscales of the PSSFP
<p>Data for analyses by graphical loglinear Rasch models of the PSFP and the PLCFP subscales of the PSSFP. Data are from Danish student teachers. Contains the following variables:</p> <p>i1 through i10 are the items from the PSSFP. Response scale is 0 = Never, 1 = Almost Never, 2 = Sometimes, 3 = Fairly Often, 4 = Very Often. Items 4, 5, 7 and 8 are reversed.</p> <p>P_level (level of latest field practice placement): 1 = level I, 2 = level II, 3 = level III</p> <p>T_Progr (teacher education program): 1 = regular, 2 = other</p> <p>Campus: 1 = campus A, 2 = campus B</p> <p>Gender: 1 = female, 2 = male</p> <p>Age: 1 = 25 years and younger, 2 = 26 years and older</p>
Predictive Executive Functioning Models Using Interactive Tangible-Graphical Interface Devices
ClinicalTrials.gov study NCT01711372. IPD Sharing: NO. Countries: 1. Publications: 1.
A graphical null model for scaling biodiversity-ecosystem functioning relationships
1. Global biodiversity is declining at rates faster than at any other point in human history. Experimental manipulations at small spatial scales have demonstrated that communities with fewer species consistently produce less biomass than higher diversity communities. Understanding how the global extinction crisis is likely to impact global ecosystem functioning requires applying these local experimental results at substantially larger spatial and temporal scales. 2. Here we propose a null model for scaling biodiversity-ecosystem functioning relationships using observed macroecological patterns. We use species-area and biomass-area curves to predict species richness – biomass relationships at multiple scales and validate these predictions with data from a Minnesota grassland and a Panamanian tropical dry forest. 3. Our null model accurately predicts species richness-biomass relationships across scales from these species-area and biomass-area relationships. However, we note two important caveats that will increase our ability to apply experimentally collected data to the global scale problem of species loss. First when ecosystem functioning is measured as per unit area (e.g., biomass m-2), as is common in biodiversity-ecosystem functioning experiments, the slope of the biodiversity ecosystem functioning relationship should decrease with increasing scale. Alternatively, when ecosystem functioning is not measured per unit area (e.g., summed total biomass), as is common in scaling studies, the slope of the biodiversity-ecosystem functioning relationship should increase with increasing spatial scale. Second, the underlying macroecological patterns of biodiversity experiments are predictably different from some naturally assembled systems. For example, in non-successional naturally assembled ecosystem, biomass is unlikely to change directionally through time. Biodiversity-ecosystem functioning experiments, however, often start from bare ground and biomass increases through time. From these underlying patterns, we would predict that the slope of the biodiversity-productivity relationship in a naturally assembled system not undergoing succession would decrease with increasing time. Alternatively, in an experiment we would predict an increase over time. 4. This paper provides a simple but novel null hypothesis for scaling any relationship between biodiversity and any ecosystem function in space and time. These predictions provide crucial insights into how and when we can extend results from small scale biodiversity experiments to naturally assembled regional and global ecosystems.
Example Dataset for the Paper "lavaangui: A Web-Based Graphical Interface for Specifying lavaan Models by Drawing Path Diagrams"
Open the record for dataset details and reuse information.
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Nicosia, Bedestan. Graphic model of the building from the south east.
<p>Nicosia, Bedestan. Graphic model of the building from the south east.</p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
A graphical null model for scaling biodiversity-ecosystem functioning relationships
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Data from: Probabilistic graphical model representation in phylogenetics
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and non-specialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution.
Predictive Executive Functioning Models Using Interactive Tangible-Graphical Interface Devices in Adults
ClinicalTrials.gov study NCT02127931. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Probabilistic graphical model representation in phylogenetics
Open the record for dataset details and reuse information.
Gene expression profiling of hepatitis B- and hepatitis C-related hepatocellular carcinoma using graphical Gaussian modeling
GEO Series GSE44074. Homo sapiens. 105 samples. Type: Expression profiling by array.
PriOmics: integration of high_throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors
GEO Series GSE253910. Homo sapiens. 360 samples. Type: Expression profiling by high throughput sequencing.
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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.