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5,805 results for “Data model”
NZESM model data of simulation u-bl274 (historical run)
<p>Subset data of NZESM historical simulation u-bl274.</p>
UKESM model data of simulation u-bm456 (historical run)
<p>Subset data of UKESM historical simulation u-bm456</p>
Modelling Avian Habitat Suitability in Boreal Forest using Structural and Spectral Remote Sensing Data
<p>Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada</p>
Open source physiological data and physiological-based kinetic model code for the chicken (Gallus gallus domesticus)
<p>This excel file and mode code (DOI:10.5281/zenodo.3603114) provides:</p> <p>1. Physiological parameters and associated inter-individual variability (sample size, mean, coefficient of variation,) for chicken (<em>Gallus gallus domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020).</p> <p>2. An R code for the generic chicken physiologically based model as well as the “soboljansen” code to carry out sensitivity analysis using sobol plots. The code for the generic model allows to run:</p> <p>a. A deterministic PBK model which represents only a single animal.</p> <p>b. A probabilistic PBK model to simulate individual differences in physiological parameters within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data. The model can be used to assess the influence of physiological parameters on the kinetics of chemicals. For PBK modelling purposes, species and chemical specific kinetics (e.g clearance, absorption rate, etc…) should be provided by the user.</p> <p>The full data collection and implementation of the models using case studies are described in (Lautz et al., 2020).</p> <p><strong>The dataset providing the physiological parameters is available in Excel.<br> The R code is presented as meta data to be implemented in R.</strong></p>
Data model for Data Scope dataset
<p>Dataset of the data model for data scopes that contains information on each class of the data scope from 30 articles from 2 journals (Digital Scholarship of the Humanities and Computational Social Sciences). </p>
Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
Data from the article "The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"
<p>Data from the article:</p> <p>"The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"</p> <p>Filipe de Sousa, Peter Civáň, João Brazão, Peter G. Foster, Cymon J. Cox</p> <p> </p> <p>These data are divided in four folders:</p> <p>* 1_36_gene_nt_alignments_&_trees - contains 36 single gene nucleotide alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 2_36_gene_aa_alignments_&_trees - contains 36 single gene amino acid alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 3_concatenated_alignments_&_trees - contains the nucleotide, codon-degenerate and amino acid alignments of 36 concatenated genes and the corresponding trees inferred from MCMC analyses on the programs p4 and phylobayes with composition homogeneous, tree-heterogeneous and site-heterogeneous models; trees correspond to figures S1-S7 on the online supplemental file.</p> <p>* 4_concatenated_ML_trees - contains the ML trees from the analyses of the concatenated datasets (nucleotide, codon degenerate and amino acid).</p> <p> </p> <p> </p> <p> </p>
Data from a model inter-comparison study to examine limiting factors in modelling Australian tropical savannas
<p>The modelling results of Whitley et al. (2016), consisting of the models BESS, BIOS2, CABLE, LPJ-GUESS, MAESPA and SPA, for five sites along the North-Australian Tropical Transect.</p> <p><strong>References</strong><br> Whitley, R., Beringer, J., Hutley, L.B., Abramowitz, G., De Kauwe, M.G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., Yu, Q., 2016. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245–3265. https://doi.org/10.5194/bg-13-3245-2016</p> <p> </p>
Model data
<p>data from modeling of Late Cretaceous by employing CESM</p>
TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, Experimental Data
<p>The data in this sheet provides the result of the exploratory experiment presented in the following paper:</p> <p>Beatriz Marín, Sofía Alarcón, Giovanni Giachetti, and Monique Snoeck. (2020) TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, in Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos (eds), Proceedings of the 14th International Conference on Research Challenges in Information Science, LNCS, Springer.</p> <p> </p>
Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study
<p>Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.</p>
Data for synthetic simulation in "Topography curvature effects in shallow-water models"
<p>Synthetic topographies and initial masses used in "Topography curvature effects in shallow-water models"</p>
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated.
High-resolution future climate data for species distribution models in Europe
<p><strong>Description</strong></p> <p>This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications.</p> <p>The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>).</p> <p>The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below.</p> <p>The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied:</p> <ol> <li><strong>Processing</strong> (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li><strong>Interpolation</strong> of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li><strong>Calculate differences</strong> between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> <p>The dimensions of the 1x1km grid (excl. the first row and column):</p> <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> <p>The longitudes and latitudes of respectively the southwest and northeast corner of the grid are:</p> <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> <p>The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project.</p> <p><strong>Variables</strong></p> <ul> <li><strong>Variable</strong> (VariableName): Unit</li> <li><strong>var1</strong> (AnnualMeanTemperature): °C</li> <li><strong>var2</strong> (AnnualAmountPrecipitation): mm year<sup>-1</sup></li> <li><strong>var3</strong> (AnnualVariationPrecipitation): coefficient of variation</li> <li><strong>var4</strong> (AnnualVariationTemperature): stdev</li> <li><strong>var5</strong> (MaximumTemperatureWarmestMonth): °C</li> <li><strong>var6</strong> (MinimumTemperatureColdestMonth): °C</li> <li><strong>var7</strong> (TemperatureAnnualRange): °C</li> <li><strong>var8</strong> (PrecipitationWettestMonth): mm</li> <li><strong>var9</strong> (PrecipitationDriestMonth): mm</li> <li><strong>var10</strong> (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li><strong>var11</strong> (AnnualMeanPotentialEvapotranspiration): mm day<sup>-1</sup></li> <li><strong>var12</strong> (AnnualMeanSolarRadiation): W m<sup>-2</sup></li> <li><strong>var13</strong> (AnnualVariationSolarRadiation): stdev</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv</strong>: climatological data layers for the 13 variables listed above</li> <li><strong>lon_1km.csv</strong>: longitudes for the 1x1km grid</li> <li><strong>lat_1km.csv</strong>: latitudes for the 1x1km grid</li> </ul>
Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river
<p>The 'Kaipara_model.mat' files contains the grid and bathymetry of a model of the Kaipara River, New Zealand, created notably in order to study turbulence in a Lagrangian frame of reference. </p> <p>The 'Dataset_Kaipara_Lagrangian.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions. </p>
Supplementary Data for Multiform: Multi-objective Evolution of Requirements Models Constrained by Formal Verification Results
<p>This data set provides supplementary material for the article "<em>Multiform: Multi-objective Evolution of Requirements Models Constrained by Formal Verification Results</em>" (to appear). It contains the following files:</p> <ul> <li><strong>experiment-input-models.zip</strong> which contains the SML input models for the EBEAS and production cell examples that were used to conduct the experiment</li> <li><strong>experiment-results.tar</strong> which contains the computed candidate SML models as well as H2 database files that contain measurements.</li> <li><strong>experiment-results.pdf</strong> which summarizes the conducted controlled experiment and results.</li> </ul> <p> </p> <p><strong>Input models</strong> (example for EBEAS)</p> <ul> <li><strong>ebeas.sml</strong> contains the actual SML input model to be evolved</li> <li><strong>ebeas.ecore</strong> contains the metamodel of the EBEAS example</li> <li><strong>ebeas.xmi</strong> contains the object system of the EBEAS example that is used for the SML realizability check</li> <li><strong>ebeas.runconfig</strong> contains the runtime configuration for ScenarioTools that binds the SML input model with the object system</li> <li><strong>ebeas.cspec</strong> contains the solution space model used by Multiform.</li> </ul> <p> </p> <p><strong>Measurements</strong> are stored in an <a href="http://h2database.com/html/main.html">H2 database</a> file. To open one of the database files for the EBEAS or production cell examples extract the appropriate zip file to a local folder, navigate to the folder in a terminal, and start H2 with the appropriate database file as follows:</p> <pre><code>java -jar h2-1.4.199.jar -url jdbc:h2:./Statistics</code></pre> <p>A web-based SQL client will open in your browser. H2 cann be obtained free of charge from their homepage.</p> <p> </p> <p>The <strong>database schema</strong> consists of three simple tables:</p> <p><strong>SMLCANDIDATESTATISTICS</strong> contains measurements for each evolved candidate SML model and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- seed id for which the measurement was taken<br> <strong>ITERATION </strong>- iteration id during whch the measurement was taken<br> <strong>CANDIDATE </strong>- unique id of the evaludated candidate SML model<br> <strong>SYNTHESISTIME </strong>- synthesis time of the evaludated candidate SML model<br> <strong>O1_SCENARIOS </strong>- objective value for o1<br> <strong>O2_FRAGMENTSRATIO </strong>- objective value for o2<br> <strong>O3_ENVFRAGMENTSRATIO </strong>- objective value for o3<br> <strong>C1_REALIZABILITY </strong>- constraint value for c1<br> <strong>C2_REACHABILITY </strong>- constraint value for c1</p> <p><strong>SMLITERATIONSTATISTICS </strong>contains aggregated statistical data for each iteration and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- seed id for which the data was aggregated<br> <strong>ITERATION </strong>- unique id of this aggregated iteration data<br> <strong>ITERATIONSUCCESSRATE </strong>- achieved success rate in this iteration<br> <strong>ACCUMULATEDSUCCESSRATE </strong>- achieved aggregated success reate until this iteration<br> <strong>ACCUMULATEDHYPERVOLUMEINDICATOR </strong>- achieved hypervolume until this iteration<br> <strong>NUMITERATIONPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models in this iteration<br> <strong>NUMACCUMULATEDPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models until this iteration<br> <strong>NUMITERATIONPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models in this iteration<br> <strong>NUMACCUMULATEDPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models until this iteration<br> <strong>ITERATIONSYNTHESISTIME </strong>- total synthesis time of this iteration<br> <strong>ACCUMULATEDSYNTHESISTIME </strong>- accumulated total synthesis time until this iteration</p> <p><strong>SMLSEEDSTATISTICS</strong> contains aggregated statistical data for each seed and consists of the following columns:<br> <strong>ALGORITHM </strong>- one of 'Random', 'NSGA2', 'tabu-75-intensify'<br> <strong>SEED </strong>- unique id of this aggregated seed data<br> <strong>SUCCESSRATE</strong>- achieved success rate in this seed<br> <strong>HYPERVOLUMEINDICATOR </strong>- achieved hypervolume in this seed<br> <strong>NUMPARETOEQUIVALENTCANDIDATES </strong>- number of pareto-equivalent candidate SML models in this seed<br> <strong>NUMPARETODOMINANTCANDIDATES </strong>- number of pareto-dominant candidate SML models in this seed<br> <strong>TOTALSYNTHESISTIME </strong>- total synthesis time of this seed</p> <p> </p> <p><strong>Please note</strong>: the database files contain data for algorithms 'tabu-50-intensify' and 'tabu-25-intensify' representing evaluation runs with different Tabu search configurations. However, these still need to be analyzed and <strong>experiment-results.pdf</strong> refers to '<strong>tabu-75-intensify</strong>' only.</p>
Data for "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations"
<p>Data, figure generation scripts, and zero-dimensional version of the 17 species Biogeochemical Flux Model (BFM17) for the paper "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations" submitted to Geoscientific Model Development. </p>
Data for "Second Critical Point in Two Realistic Models of Water"
<p>Data associated to the publication "Second Critical Point in Two Realistic Models of Water" to appear in Science in 2020.</p> <p> </p> <p>e-rho-2005.tgz files with potential energy and density for the model TIP4P-2005 (N=300,500,1000)</p> <p><a href="https://zenodo.org/api/files/6f0afff6-e0e8-44df-9a5c-8fbb332d343b/e-rho-tip4pice.tgz">e-rho-tip4pice.tgz </a> files with potential energy and density for the model TIP4P-Ice (N=300,500,1000)</p> <p>sk-dat.tgz files with the structure factor for TIP4P-2005 (N=36424) and TIP4P-Ice (N=10000)</p> <p>mdp-gro-top-2005.tgz input files for all studied state point in GROMACS format for TIP4P-2005 (mdp, gro, top) (N=300,500,1000)</p> <p>mdp-gro-top-Ice.tgz input files for all studied state point in GROMACS format for TIP4P-Ice (mdp, gro, top) (N=300,500,1000)</p> <p>large-systems.tgz input files for all studied state point in GROMACS format for TIP4P-Ice (N=10000) and TIP4P-2005 (N=36424)</p> <p> </p>
Data citation for a forward stratigraphic-based porosity and permeability model developed for the Volve field, Norway.
<p>The data, models, and script presented here are those used for developing a forward stratigraphic simulation. The data include: 24 suits of well logs, seismic data, forward stratigraphic simulation scenarios of the shallow marine depositional setting, synthetic wells derived from the stratigraphic model, and 3-D reservoir models in Eclipse and RMS formats. In addition, a short script from the property calculator tool in Petrel, which is was used to classify lithofacies-associations in the stratigraphic model is also provided. The Petrel software license and code used in GPM software to undertake these forward stratigraphic simulations cannot be provided, because Schlumberger, who are the developers of the software do not allow its code to be shared in any publication.</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.