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481 results for “network model”
A Survival Analysis based Volatility and Sparsity Modeling Network for Student Dropout Prediction
<p>KDDCup15.rar and XuetangX.rar are the metadata used in the paper "A Survival Analysis based Volatility and Sparsity ModelingNetwork for Student Dropout Prediction". Both of them have be drawn from the largest MOOC platform in China, XuetangX (see https://www.xuetangx.com/). If any interested parties want to fetch the original dataset, they may follow the URLs bellow:</p> <p>KDDCup 2015 dataset is available at <a href="https://www.biendata.xyz/competition/kddcup2015/data/">https://www.biendata.xyz/competition/kddcup2015/data/</a>.</p> <p>XuetangX dataset is available at <a href="http://moocdata.cn/data/user-activity">http://moocdata.cn/data/user-activity</a>.</p>
4D-Var data assimilation experiment of the Lorenz 96 model using an adjoint model of a neural network surrogate model
<p>These data are the output of the 4D-Var data assimilation experiment of the Lorenz96 model using an adjoint model of a neural network surrogate model.<br> The details are described in Nishizawa (2022).<br> </p>
Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling
<p>This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal.</p> <p>This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy.</p> <p>Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.</p>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Development of a Multi-Level Dynamic Model to Measure the Resilience Level of Transportation Infrastructure Networks
<p>The recent increase in disasters is making the largest critical infrastructure system namely the transportation infrastructure system susceptible to unexpected damage. Discontinuation of services provided by transportation infrastructures will create significant societal, economic, and collateral damages. Therefore, this study aims to identify dimensions to measure the resilience of the transportation infrastructures. This study also aims to develop a model to measure the resilience of the transportation infrastructures resilience. To fulfill the aims of this study, a questionnaire was developed which was supported by a comprehensive literature review. 92 valid responses were received and analyzed qualitatively and quantitatively. Statistically significant variables were used to develop a resilience measurement tool. The developed tool will provide relative resilience measures for multiple projects which will help in identifying the most vulnerable segment of the transportation infrastructure network. Exploratory factor analysis (EFA) was performed to identify the constructs and structural equation modeling (SEM) was used to develop the model. Without previous experience in reconstruction works, handling integrated assets becomes very critical. Also, such inexperience makes it difficult to handle emergency resources properly. However, such issues regarding integrated assets can be resolved by investing in locating integrated assets away from the roadways, so if a break in a railroad crossing or utility line occurs or emergency repairs are needed, the impact on the roadway operations can be minimized. To avoid issues related to access to previous disaster data for the roadway this study suggess investing in preparing an interactive online platform for recording and reviewing data related to disasters as well as previous resilience enhancing activities for the roadway with easy access credentials. The findings of this study will support practitioners and decision-makers in investing in the appropriate resilience enhancement activity project for funding and investment.</p>
Neural network models for processing of ultrasound rodents recordings
<p>Neural network models for processing of ultrasound rodents recordings to be used with custom software dedicated to detection and classification of USV.</p>
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
<p>Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".</p>
Neural networks and surface models of Itokawa and Bennu
<p>This dataset comprises the neural networks and surface models of asteroids Itokawa and Bennu, as presented in the paper "Asteroid-NeRF: A deep-learning method for 3D surface reconstruction of asteroids" by Shihan Chen, Bo Wu, Hongliang Li, Zhaojin Li, and Yi Liu.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
Spatiotemporal Modeling of Mitochondrial Network Architecture
<p>Mitochondrial fusion events and their classification into three categories (tip-tip, tip-side, side-side) acquired on widefield, confocal, instant structured illumination and structured illumination microscopes.</p> <p>This data was used as comparison to modelled, theoretical fusion rates and their contributions to the maintenance of mitochondrial network morphologies.</p>
Dataset to accompany publication "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"
<h2>Dataset description</h2> <p>This dataset displays the raw data for the manuscript "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"<strong> </strong>published in <em>Precision Chemistry </em>on 06 December 2023 (DOI: <a title="DOI URL" href="https://doi.org/10.1021/prechem.3c00078">10.1021/prechem.3c00078</a>).</p> <p> </p>
The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network
<p>To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552) </p> <p>Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model</p> <p>Nominal.txt: the chemical network adopted in the model</p> <p>modify_chem.py: the additional adjustments of the reactions</p> <p>Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively</p> <p>reading_data.py: the methods to read the output files</p> <p> </p> <p>*Errata: </p> <p>1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm"</p> <p>2) R329 in Supplemental Table: should have been removed</p> <p>The errata do not affect the results of this study.</p>
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Excitation wave propagation on London street network. Oregonator model.
<p>Supplementary material to paper </p> <p>Andrew Adamatzky, Neil Phillips, Roshan Weerasekera, Michail-Antisthenis Tsompanas, and Georgios Ch. Sirakoulis</p> <p>Street map analysis with excitable chemical medium. Phys. Rev. E . Accepted 19 June 2018</p> <p>https://journals.aps.org/pre/accepted/7c072R1eD7e1c71d762165a4977a6cb036ce6e12e</p> <p> </p> <p>Files:</p> <p> </p> <p>London_Eps_0_02_Fi_0_065_long --> $\phi=0.065$</p> <p>London_Eps_0_02_Fi_0_075_long --> $\phi=0.075$</p> <p>Time lapsed snapshots of a single wave-fragment recorded every 150\textsuperscript{th} step of numerical integration.</p> <p>lapse_eps_0.02_fi0.064.jpg lapse_eps_0.02_fi0.063.jpg lapse_eps_0.02_fi0.062.jpg lapse_eps_0.02_fi0.061.jpg lapse_eps_0.02_fi0.06.jpg lapse_eps_0.02_fi0.059.jpg lapse_eps_0.02_fi0.058.jpg lapse_eps_0.02_fi0.056.jpg lapse_eps_0.02_fi0.055.jpg lapse_eps_0.02_fi0.066.jpg lapse_eps_0.02_fi0.054.jpg lapse_eps_0.02_fi0.052.jpg lapse_eps_0.02_fi0.05.jpg lapse_eps_0.02_fi0.067.jpg lapse_eps_0.02_fi0.068.jpg lapse_eps_0.02_fi0.069.jpg lapse_eps_0.02_fi0.07.jpg lapse_eps_0.02_fi0.071.jpg lapse_eps_0.02_fi0.065.jpg lapse_eps_0.02_fi0.074.jpg lapse eps_0.02_fi0.076.jpg lapse_eps_0.02_fi0.073.jpg lapse eps_0.02_fi0.077.jpg lapse_eps_0.02_fi0.075.jpg lapse_eps_0.02_fi0.072.jpg</p> <p>Coverage frequency is visualised in the images below (see details in the paper)</p> <p>coverageFrequency eps_002_fi0076.jpg coverageFrequency eps_002_fi0077.jpg coverageFrequency eps_002_fi0078.jpg coverageFrequency eps_002_fi0079.jpg coverageFrequency eps_002_fi0080.jpg coverageFrequency eps_002_fi0081.jpg coverageFrequency eps_002_fi0082.jpg coverageFrequency eps_002_fi0083.jpg coverageFrequency eps_002_fi0084.jpg coverageFrequency eps_002_fi0085.jpg coverageFrequency_eps_0.02_fi0.07.jpg coverageFrequency_eps_0.02_fi0.052.jpg coverageFrequency_eps_0.02_fi0.054.jpg coverageFrequency_eps_0.02_fi0.055.jpg coverageFrequency_eps_0.02_fi0.056.jpg coverageFrequency_eps_0.02_fi0.058.jpg coverageFrequency_eps_0.02_fi0.059.jpg coverageFrequency_eps_0.02_fi0.061.jpg coverageFrequency_eps_0.02_fi0.062.jpg coverageFrequency_eps_0.02_fi0.063.jpg coverageFrequency_eps_0.02_fi0.064.jpg coverageFrequency_eps_0.02_fi0.065.jpg coverageFrequency_eps_0.02_fi0.066.jpg coverageFrequency_eps_0.02_fi0.067.jpg coverageFrequency_eps_0.02_fi0.068.jpg coverageFrequency_eps_0.02_fi0.069.jpg coverageFrequency_eps_0.02_fi0.071.jpg coverageFrequency_eps_0.02_fi0.072.jpg coverageFrequency_eps_0.02_fi0.073.jpg coverageFrequency_eps_0.02_fi0.074.jpg coverageFrequency_eps_0.02_fi0.075.jpg coverageFrequency_eps_002_fi0050.jpg coverageFrequency_eps_002_fi0060.jpg</p> <p>Dynamics of integral excitation</p> <p>activity eps_0.02_fi0.050.txt activity eps_0.02_fi0.052.txt activity eps_0.02_fi0.054.txt activity eps_0.02_fi0.055.txt activity eps_0.02_fi0.056.txt activity eps_0.02_fi0.057.txt activity eps_0.02_fi0.058.txt activity eps_0.02_fi0.059.txt activity eps_0.02_fi0.060.txt activity eps_0.02_fi0.061.txt activity eps_0.02_fi0.062.txt activity eps_0.02_fi0.063.txt activity eps_0.02_fi0.064.txt activity eps_0.02_fi0.065.txt activity eps_0.02_fi0.066.txt activity eps_0.02_fi0.067.txt activity eps_0.02_fi0.068.txt activity eps_0.02_fi0.069.txt activity eps_0.02_fi0.070.txt activity eps_0.02_fi0.071.txt activity eps_0.02_fi0.072.txt activity eps_0.02_fi0.073.txt activity eps_0.02_fi0.074.txt activity eps_0.02_fi0.075.txt activity eps_0.02_fi0.076.txt activity eps_0.02_fi0.077.txt activity eps_0.02_fi0.078.txt activity eps_0.02_fi0.079.txt activity eps_0.02_fi0.080.txt activity eps_0.02_fi0.081.txt activity eps_0.02_fi0.082.txt activity eps_0.02_fi0.083.txt activity eps_0.02_fi0.084.txt activity eps_0.02_fi0.085.txt</p> <p>Abstract </p> <p>Belousov-Zhabotinsky (BZ) thin layer solution is a fruitful substrate for designing unconventional computing devices. A range of logical circuits, wet electronic devices, and neuromorphic prototypes have been constructed. Information processing in BZ computing devices is based on interaction of oxidation (excitation) wave fronts. Dynamics of the wave fronts propagation is programmed by geometrical constraining and interaction of colliding wave fronts is tuned by illumination. We apply the principles of BZ computing to explore a geometry of street networks. We use two-variable Oregonator equations, the most widely accepted and verified in laboratory experiments model of BZ, to study propagation of excitation wave fronts for a range of excitability parameters, gradual transition from excitable to sub-excitable to non-excitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities and characterising geometry of street networks.</p> <p>Earlier draft (missing some new findings presented in PRE paper) is on arXiv: </p> <p>https://arxiv.org/abs/1803.01632</p> <p> </p> <p> </p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
GIS data - Model sítě vodních toků v ČR | GIS data - Water Stream Network Model for the Czech Republic
<p>GeoTIFF layers (8 x 8 m) containing a modeled network of watercourses in the landscape of the Czech Republic: (1) "streams_def_cr.tif" layer - network of natural watercourses with Strahler order determination; (2) "vodni_toky.tif" layer - model of potentially navigable watercourses. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
GIS data - Model přirozené komunikační sítě pro území ČR | GIS data - Model of Natural Path Network for the Czech Republic
<p>GeoTIFF layer (8 x 8 m) containing a model of natural routes through the landscape of the Czech Republic. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
Modelling riparian forest distribution and composition to entire river networks
<p><strong>Aim: </strong>Developing a methodology to map the distribution of riparian forests to entire river networks and determining the main environmental factors controlling their spatial patterns.</p> <p><strong>Location: </strong>Cantabrian region, northern Spain.</p> <p><strong>Methods: </strong>We mapped the riparian forests at a physiognomic and phytosociological levels by delimiting riparian zones and generating vegetation distribution models based on remote sensing data (Landsat 8 OLI and LiDAR PNOA). We built virtual watersheds to define a spatial framework where the catchment environmental information can be routed to each river reach, jointly with the vegetation map. In order to determine the drivers playing a significant role on the observed spatial patterns in the riparian forest we modelled interactions between these datasets of environmental information and riparian vegetation by using the Random Forest algorithm.</p> <p><strong>Results: </strong>The modelling results obtained reproduced a reliable variation of riparian forest structure and composition across Cantabrian watersheds. The produced maps were highly accurate, with more than a 70% overall accuracy for the forest occurrence. A clear differentiation between Eurosiberian (91E0 and 9160 habitats) and Mediterranean (92E0) riparian forests was shown on both sides of the mountain range. Topography and land use were the main drivers defining the distribution of riparian forest as a physiognomic unit. In turn, altitude, climate and percentage of pasture were the most relevant factors determining their composition (phytosociological approach).</p> <p><strong>Conclusions: </strong>Our study confirms that the anthropic control ultimately defines the distribution of the vegetation in the riparian area at a regional to local scale. Human disturbances constrain the extension of forest patches across their potential distribution defined by topoclimatic boundaries, which establish a clear limit between Mediterranean and Eurosiberian biogeographical regions.</p>
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]
<p>This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>.</p> <p>Source code can be downloaded at: <a href="https://github.com/aramis-lab/AD-DL">https://github.com/aramis-lab/AD-DL</a></p> <p>Also, single files can be obtained at: <a href="https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/">https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/</a></p> <p>The structure of the compressed file is as follows:</p> <p>clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier</p> <p>We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em></p> <p>For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.</p>
ScienceDex guides
Understand access before you commit
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.