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5,805 results for “Data model”
Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"
<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al. (2022): "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length".</p>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Simulation data results from SEAMANCORE model
<p>This dataset contains simulation data from the SEAMANCORE model for a region in the Indo-Pacific.</p> <p>Four different management alternatives were examined with 1100 model runs each to account for parameter uncertainty.</p> <p>This dataset was created by running the SEAMANCORE model (https://doi.org/10.5281/zenodo.7155783) with the different input parameters (input_parameters.zip). </p> <p>The results (SEAMANCORE.zip) are daily time series of proportion of benthos groups, biomass of fish functional groups, and fished biomass in the investigated area over 2280 days (~6 years) for the four different management alternatives and 1100 parameter specifications each.</p>
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
<p>In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.</p>
Data and codes for: A link model approach to identify congestion hotspots
<p>Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
SSP2017 - Experiment Data - Towards Extracting Realistic User Behavior Models
<p>This package contains the monitoring data, the ideal behavior models, computed clustering results for behavior models based on the monitoring data, and the interpretation of the analysis results. We processed these data with the following two tooling sources:</p> <p>Experiment setup: https://doi.org/10.5281/zenodo.883069</p> <p>Analysis software: https://doi.org/10.5281/zenodo.883061</p>
Distributed rewiring model data
<p>This repository contains the data that supports "Distributed rewiring model for complex networking: the effect of local rewiring rules on final structural properties" in PLOS one journal.</p>
Data for "Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model"
<p>The data files arranged here correspond to the data used in the paper: “Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model”, submitted to <em>Water</em>.</p> <p>The data organized as follows:</p> <ul> <li>Data_type_20150912_time_steps.mat: the rainfall data for 3 different products of the X-band radar (FIR filter, a=200, b=1.6; FIR filter, a=150, b=1.3; simple filter, a=150, b=1.3) for the event of 12-13 September 2015, over an area of 64 km x 64 km.</li> <li>Data_type_Event_time_steps.mat: X-band radar data (FIR filter, a=150, b=1.3) for the events of 16 September 2015 and 5-6 October 2015, over an area of 64 km x 64 km.</li> <li>Sub-catchment_name_Data_type_Event.txt: the rainfall series for each of 26 sub-catchments of the model, for 3 different types of rainfall data (C-band, X-band and rain gauges) for the events of 12-13 September 2015, 16 September 2015 and 5-6 October 2015.</li> <li>X-band_Pixels_Event.txt: the rainfall series for all 6 X-band radar pixels corresponding to the 6 rain gauges for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015).</li> <li>X-Band_Optim 20150916_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with X-band data for the 16 September 2015 event, with the implementation of the tool mimicking the regulation optimization.</li> <li>Data_type_Event_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with 3 different types of rainfall data (C-band, X-band and rain gauges) for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015), without the implementation of the tool mimicking the regulation optimization.</li> </ul> <p>The original C-band radar data remains property of Météo-France and was provided to the authors for this research study, without any possibility of data disclosure.</p> <p>The details on how the rainfall series were generated over each sub-catchment could be found in the paper.</p> <p>The authors greatly acknowledge partial financial supports of the Chair “Hydrology for resilient cities” endowed by Veolia, and of the Department of Science and Technology of the Brazilian Army. The authors are thankful to M Bernard Urban (Météo-France) for providing access to the C-band radar data and documentation in the framework of the INTERREG NWE RainGain project.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Variability of Global Fire Emissions - Data and Model Code
<p>Netcdf files including all relevant data for the manuscript entitled "Trends and variability of global fire emissions due to historical anthropogenic activities", submitted to Global Biogeochemical Cycles in 2017. </p> <p>FINALv2_presentday_2002-2009.nc: Monthly fire area burned and carbon emissions data from FINAL.2 for the years 2002 through 2009</p> <p>FINALv2C_*_1700-2009.ts.nc: Historical time series of monthly area burned and carbon emissions for natural, secondary, crop and pasture land cover for years 1700 to 2009</p> <p>vegn_fire.F90: The main module of FINAL.2 in the GFDL LM3</p>
Digital 3D Model Dataset from Thingiverse Data
<p>Digital model data as STL files, acquired from thingiverse by random probing of available things. Geometrical analysis and rendering as PNG and GIF performed. Pre-sliced machine instructions as GCode generated using slic3r. AMF files are also part of the dataset, these files were generated from the STL files.</p>
Data for Developing Machine Learning Models to Predict Base Resistance of Pile Foundation
<p>This data was collected from 86 static pile load tests across 37 different high-rise buildings in Vietnam, especially soft soil region in Mekong Delta (Ho Chi Minh City). The data was used to develop machine learning models to predict base resistance of piles. Further details can be found in publication: "<strong>Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments</strong>", Link: https://www.mdpi.com/2673-7094/4/2/25.</p> <p>Recommended citation: Nguyen, Thanh T., Viet D. Le, Thien Q. Huynh, and Nhu H.T. Nguyen. 2024. "Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments" <em>Geotechnics</em> 4, no. 2: 447-469. https://doi.org/10.3390/geotechnics4020025</p> <p> </p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></p>
Data and model weights for a series of antibody language models
<p>This repo contains the sequence dataset for finetuning and model weights including pre-trained meta model and a series of finetuned antibody language models.</p> <p>The antibody language models were used in the paper "<em>Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy"</em> for antibody sequence embedding.</p>
Data from: A theoretical model for host-controlled regulation of symbiont density
<p>There is growing empirical evidence that hosts (such as insects and corals) actively control the density of their mutualistic symbionts according to their requirements. Such active regulation can be facilitated by compartmentalisation of symbionts within host tissues, which confers a high degree of control of the symbiosis to the host. Here, we build a general theoretical framework to predict the underlying ecological drivers and evolutionary consequences of host-controlled endosymbiont density regulation for a mutualistic association between a host and a compartmentalised, vertically transmitted symbiont. Building on the assumption that the costs and benefits of hosting a symbiont population increase with symbiont density, we use state-dependent dynamic programming to determine an optimal strategy for the host, i.e., that which maximises host fitness, when regulating the density of symbionts. Simulations of active host-controlled regulation governed by the optimal strategy predict that the density of the symbiont should converge to a constant level during host development, and following perturbation. However, a similar trend also emerges from alternative strategies of symbiont regulation. The strategy which maximises host fitness also promotes symbiont fitness compared to alternative strategies, suggesting that active host-controlled regulation of symbiont density could be adaptive for the symbiont as well as the host. Adaptation of the framework allowed the dynamics of symbiont density to be predicted for other host-symbiont ecologies, such as for non-essential symbionts, demonstrating the versatility of this modelling approach.</p>
Raw data from the pCUT+MC approach for the antiferromagnetic Heisenberg square lattice bilayer model with (non-frustrating) long-range interactions
<p>This directory contains the data from the pCUT+MC approach for the antiferromagnetic Heisenberg square lattice bilayer model with (non-frustrating) long-range interactions.</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>The data is published in P. Adelhardt, J. A. Koziol, A. Langheld, and K. P. Schmidt, "Monte Carlo based techniques for quantum magnets with long-range interactions", <a href="https://arxiv.org/abs/2403.00421">arXiv:2403.00421</a></p>
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Model output: CICE6-WIM Antarctic sea ice data (2010--2019)
<p>CICE6 model with waves-in-ice module (WIM) output analysed with k-means clustering to identify the Antarctic marginal ice zone. Daily data at a 1-degree horizontal resolution of the variables used to inform k-means and the physical processes which drive the Antarctic marginal ice zone are included. The model was spun-up over 2005–2009 and outputs were analysed over 2010–2019.</p> <p> </p> <p>For further details, see </p> <p>Day, N. S., Bennetts. L. G., O'Farrell, S. P., Alberello, A., & Montiel, F. (2023) Analysis of the Antarctic Marginal Ice Zone Based on Unsupervised Classification of Standalone Sea Ice Model Data.</p> <p> </p> <p>Data files:</p> <ul> <li><code>iceh_{year}.nc</code>: gridded NetCDF files containing the training variables for k-mean clustering and the fitted clusters.</li> <li><code>forcing_variables_2010_2019.csv</code>: flat file containing variables of CICE6-WIM forcing and sea ice dynamics.</li> <li><code>fsd_processes_2010_2019.csv</code>: flat file containing each term of the prognostic floe size distribution model in CICE6.</li> </ul> <p> </p> <p>Scripts:</p> <ul> <li><code>ice_in</code>: CICE namelist</li> <li>Waves-in-Ice Module: <ul> <li><code>ice_floe.F90</code>: Appends the WIM to CICE6</li> <li><code>m_prams_waveice.F90</code>: wave-ice parameters</li> <li><code>m_waveattn.F90</code>: wave attenuation from sea ice</li> <li><code>m_fzero.F90</code>: useful mathematical functions and solvers</li> <li><code>m_waveice.F90</code>: wave-ice physics</li> </ul> </li> </ul> <p>To implement the WIM into CICE6, subroutines within ice_floe.F90 should be called prior calling the floe size distribution subroutines in ice_step_mod.F90. For further details/assistance in implementing the WIM please contact Noah Day (<code>noah.day@adelaide.edu.au</code>).</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.