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279 results for “model comparison”
Data for: The Precipitation Response to Warming and CO2 Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models
<p>Data to reproduce figures in manuscript "The Precipitation Response to Warming and CO$_2$ Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models" submitted to GRL</p>
MD data for the article "Structure comparison of beta amyloid peptide Aβ 1-42 isoforms. Molecular dynamics modeling" by Anna P. Tolstova, Alexander A. Makarov, Alexei A. Adzhubei.
<p>There are CMD and REMD trajectories for Aβ isoforms discussed in the paper together with final coordinate files for these trajectories. The resulting dataset of modeled structures includes wild type Aβ42, isoD7, pS8, D7H and H6R-Aβ42, and wild type Aβ16, isoD7, pS8, D7H and H6R-Aβ16.</p>
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Plots
<p>A collection of all plots generated for deriving the conclusions seen in "<span><span>A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems</span></span>".</p>
X-SHiELD model verification figures, related to: "The Precipitation Response to Warming and CO$_2$ Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models"
<p>Figures comparing differrent fields in X-SHiELD with observations. Similar information can be recived at https://extranet.gfdl.noaa.gov/%7EAlex.Kaltenbaugh/verification/</p>
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
Antibody-Antigen Models for McCoy 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"
<p>Up to the top 20 models generated for each method tested in the 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"</p>
Model Comparison Benchmark Datasets, Results, and Peptide MD Trajectory
<p>Contents of this database include:</p> <ul> <li>aib9.out.zip: zipped file containing raw output of the Aib9 OpenMM MD simulation</li> <li>aib9_openmm.zip: zipped folder containing all input files for the Aib9 OpenMM MD simulation, as well as the generated trajectory</li> <li>data_input.zip: zipped folder containing input datasets for the GMM and Aib9 numerical experiments</li> <li>data_output.zip: zipped folder containing output data and experimental results for three generative models (NS, CFM, DDPM) on the input datasets, included for posterity (file names do not synchronize with most recent repository naming conventions)</li> </ul> <p>The files are automatically retrieved and unzipped via the data_accessor notebook in the GitHub repository.</p> <p> </p>
Figure 5. Branch length comparisons across models and taxa sets for 62 in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 5. Branch length comparisons across models and taxa sets for 62 taxa. All common bipartition branch lengths from each of the three taxa set analyses (pruned to the 62 taxa) plotted against the 62t analysis 3p model bipartition branch lengths. Confidence intervals (CI) based on MCMC variation in the most data complete analysis, are approximated by dashed lines.
Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
Code for: Comparison and interpretability of machine learning models to predict severity of chest injury
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at non-trauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Materials and Methods:</b> A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net regularization (EN), extreme gradient boosted machines (XGB), and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> Of 8,952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Discussion: </b>The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion:</b> NLP and ML methods to populate trauma registries for quality analyses are feasible.</span></span></span></span></span></span></span></span></span></span></span></p>
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</p>
FIGURE 6 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 6. MaxEnt model outputs for Ampedus samedovi. Minimum Training Presence threshold is applied to outputs.
FIGURE 5 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 5. MaxEnt model outputs for Ampedus platiai. Minimum Training Presence threshold is applied to outputs.
FIGURE 2 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 2. Distributions and collecting localities of Ampedus platiai and Ampedus samedovi. Red: Distribution of only A. platiai in the provinces, Blue: Distribution of only A. samedovi in the provinces, Yellow: Distribution of A. platiai and A. samedovi in the provinces (The map is designed in ArcGis 10.2).
FIGURE 1 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 1. Habitus and aedeagi photos of examined species. A–B. Ampedus platiai, C–D. A. samedovi, E–F. A. pomonae (Aedeagi of A. platiai and A. samedovi are redrawn from Kabalak 2010 and aedeagus of A. pomonae is redrawn from Platia 1994.). BML: Basal struts of median lobe, BP: Basal piece, ML: Median Lobe, PDT: Paramere distal tooth, PR: Paramere.
Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling
<p>Input and output files for the simulations presented in the Nuclear Fusion paper "Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling"</p>
Modulation of 5-LOX activity and comparison with COX-2 activity by (poly)phenols in cellular models.
<p>5-Lipoxygenase (5-LOX) plays a key role in inflammation through the biosynthesis of leukotrienes and other lipid mediators. Current evidence suggests that dietary (poly)phenols exert a beneficial impact on human health through anti-inflammatory activities. Their mechanisms of action have mostly been associated with the modulation of pro-inflammatory cytokines (TNF-α, IL-1β), prostaglandins (PGE<sub>2</sub>), and the interaction with NF-κB and cyclooxygenase 2 (COX-2) pathways. Much less is known about the 5-lipoxygenase (5-LOX) pathway as a target of dietary (poly)phenols. This systematic review aimed to summarize how dietary (poly)phenols target the 5-LOX pathway in preclinical and human studies. The number of studies identified is low (5, 24, and 127 human, animal, and cellular studies, respectively) compared to the thousands of studies focusing on the COX-2 pathway. Some (poly)phenolics such as caffeic acid, hydroxytyrosol, resveratrol, curcumin, nordihydroguaiaretic acid (NDGA), and quercetin have been reported to reduce the formation of 5-LOX eicosanoids in vitro. However, the in vivo evidence is inconclusive because of the low number of studies and the difficulty of attributing effects to (poly)phenols. Therefore, increasing the number of studies targeting the 5-LOX pathway would largely expand our knowledge on the anti-inflammatory mechanisms of (poly)phenols.</p>
Data for "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"
<p>All data recorded for reflectance and e-folding depth measurements associated with "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"</p>
Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments
<p>This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [<em>NSE</em>] > 0.5), whereas SWAT generally yielded unsatisfactory results (<em>NSE</em> < 0.5) and tended to overestimate daily streamflow by up to 27% (<em>PBIAS</em>). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.</p> <p>This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results.</p> <p>The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.</p>
mW model for SPONGE and LAMMPS comparison
<p>This is the dataset to compare the mW model in SPONGE with that in LAMMPS</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.