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Dataset results
481 results for “network model”
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Parallel generation of extensive vascular networks with application to an archetypal human kidney model
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Data for: Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients
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Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
<pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
SHYFEM set-up for model driven optimization of the tide gauge monitoring network in the Venice Lagoon
<p>This database include all configuration files, script and data for running the<br> simulations and elaborate the results presented in the work entitled "Model-driven<br> optimization of coastal sea observatories through data assimilation in a finite<br> element hydrodynamic model (SHYFEM v. 7_5_65)" to be submitted in Geoscientific Model<br> Development (GMD).</p>
Dataset for: Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates
<p>Published in:<br> Nature Biomedical Engineering. doi: 10.1038/s41551-020-0566-1.</p> <p>Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates</p> <p>Ian S. Kinstlinger (1), Sarah H. Saxton (2), Gisele A. Calderon (1), Karen Vasquez Ruiz (1), David R. Yalacki (1), Palvasha R. Deme (1), Jessica E. Rosenkrantz (3), Jesse D. Louis-Rosenberg (3), Fredrik Johansson (2), Kevin D. Janson (1), Daniel W. Sazer (1), Saarang S. Panchavati (1), Karl-Dimiter Bissig (4), Kelly R. Stevens (2,5), and Jordan S. Miller (1)</p> <p>1 Department of Bioengineering, Rice University, Houston, TX, USA.<br> 2 Department of Bioengineering, University of Washington, Seattle, WA, USA.<br> 3 Nervous System, Palenville, NY, USA.<br> 4 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.<br> 5 Department of Pathology, University of Washington, Seattle, WA, USA</p> <p>Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D-printing techniques. Here we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging as a result of fluid convection through these networks and of diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function.</p>
MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
<p>Deep neural networks have shown promising success towards the classification and retrieval tasks for images and text data. While there have been several implementations of deep networks in the area of computer graphics, these algorithms do not translate easily across different datasets, especially for shapes used in product design and manufacturing domain. Unlike datasets used in the 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D models do not yield themselves to neat distinct classes. The current study looks at an improved form of the 3D shape deep learning algorithm for classification and retrieval through the use of techniques such as relaxed classification, use of prime angled camera angles for capturing feature detail and transfer learning for reducing the amount of data and processing time needed to train shape recognition algorithms. The proposed algorithm (MVCNN++) builds on top of multi-view convolutional neural network (MVCNN) algorithm, improving its efficacy for manufacturing part classification by enabling use of part metadata, yielding an improvement of almost 6% over the original version. With the explosive growth of 3D product models available in publicly available repositories, search and discovery of relevant models is critical to democratizing access to design models.</p>
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models
<p>This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models".</p> <p>The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each.</p> <p>There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches.</p> <p>We also provide a Python code to read and visualize the .npy files.</p>
Computational modeling of hemoglobin saturation heterogeneity in capillary networks
<p>This repository contains the C++ code based on OpenFOAM used for simulating oxygen transport with moving red blood cells. The OpenFOAM cases used to generate all results in the research article "The heterogeneity of hemoglobin saturation in capillaries and its relation to red blood cell transit time" are included.</p> <p>The archive 'code-axisymmetric.tgz' contains the code for the simulations in axisymmetric domains. This code works with OpenFOAM 2.1.1.</p> <p>The archive 'code-parallel_capillaries.tgz' contains the code for the simulations with parallel capillaries. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-graph.tgz' contains the simulation code for the simulations in reconstructed capillary networks. The postprocessing and plotting script are also in this archive. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-flow_reconstruction.tgz' contains the code for the flow reconstruction algorithm.</p> <p>The remaining archives contain the OpenFOAM cases that were used to run the oxygen transport simulations reported in the research article "The Heterogeneity of Hemoglobin Saturation in Capillary Networks and its Relation to Red Blood Cell Transit Time".</p>
Supplemental 3D Model Data - New insights into the evolutionary history of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network (SPIERSView and VAXML format)
<p>Three-dimensional reconstruction models of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network in SPIERSView and VAXML format. 2D and 3D (Red/Cyan) images also provided as a PDF.</p> <p>Notes:</p> <ol> <li>SPIERSView file (.SPV) models can conveniently be viewed using the SPIERSView software, freely available in both Windows and Mac versions from http://www.spiers‐software.org. However, note that low-performance computers may not possess a sufficiently powerful graphics card to render and rotate the model.</li> <li>VAXML file format models are saved as a ZIP-compressed VAXML datasets. VAXML uses one or more .STL files to define the geometry of objects that comprise the dataset, together with one .VAXML file that provides metadata on the dataset as a whole, and specifies how the .STL files should be put together. We recommend using the free SPIERS software to view this model format (http://spiers-software.org/). However, .STL files can be opened independently in several freely available software programs (e.g. MeshLab, Blender). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm.</li> </ol>
Simulated results from an agent-based model examining inequality and innovation in social networks
<p>Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation; and that the highest performing agents tend to occupy more central network positions. Moreover, we show that every network factor which facilitates innovation leads to a proportional increase in inequality of performance, creating "genius effects" among otherwise "dumb" agents in both idealized and real-world networks.</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Prediction model of the temporal dynamics of severe pest cashew Anacampsis phytomiella using artificial neural networks
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Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Extra-P Version Used for Noise-Resilient Empirical Performance Modeling with Deep Neural Networks
<p>This is the Extra-P source code that was used for the analysis and evaluation of the IPDPS 2021 paper "Noise-Resilient Empirical Performance Modeling with Deep Neural Networks". It also contains the checkpoints and saved models for the DNN part of the adaptive modeler as well as the gathered synthetic evaluation data.</p>
Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
<p>This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD". </p>
A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network
<p>The file uploaded provides input data for the paper "A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network".</p>
Stacked cross correlation functions for the MeSO-net network and derived 3D Vs model
<p>This dataset contains two major types of data.</p> <p>1) The yearly stacked cross-correlation functions between 296 MeSO-net stations covering the Kanto basin, Japan.</p> <p>2) A 3D radially anisotropic Vs model for the Kanto basin. The grid increment for the longitude and latitude is 0.01 degrees and that for the depth is 0.1 km.</p> <p>The complete station list for the 296 MeSO-net stations can be found on the Github page of https://github.com/chengxinjiang/Jiang_Kanto_anisotropy. </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.