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5,805 results for “Data model”
McMurdo Dry Valleys Nitrogen and Phosphorus Dynamics in Green Creek, January 1995. Field and Modeling data
McMurdo Dry Valleys, Antarctica, contain numerous glacial meltwater streams that drain into lakes on the valley floors. Many of the streams have abundant perennial mats of filamentous cyanobacteria. The algal mats grow during streamflow in the austral summer and are in a dormant freeze-dried state during the rest of the year. NO3 and soluble reactive P (SRP) concentrations were lower in streams with abundant algal mats than in streams with sparse algal mats. Here we describe and distribute the data associated with this study: An experimental injection of LiCl, NaNO3, and K3PO4 was conducted in Green Creek, which has abundant algal mats. Substantial hyporheic exchange occurred. A model in which PO4 uptake occurred only in the main channel and NO3 uptake occurred in the main channel and in the hyporheic zone. Green Creek (Gooseff, 2015) aerial flowing into Fryxell
Simulation data of barrier-shrub interactions from the Barrier3D model
Barrier3D (version 1.0) simulation results of shrub expansion and barrier migration behavior and morphology across a range of input conditions. Simulations vary by the presence or absence of shrubs, the characteristic dune growth rate (0.3-0.9 yr-1), relative sea-level rise rate (3-15 mm/yr), the maximum flow reduction coefficient (0.05-0.35), and the width of initial barrier morphology. Each row in the spreadsheet represents a unique model simulation, and each simulation belongs to one of two different experiments: 1) Barrier_evolution, which is designed to assess the ways in which shrubs alter barrier morphology and the rate and style of barrier retreat by running simulations with and without shrubs across broad ranges of relative sea-level rise rates and dune growth rates; and 2) Shrub_expansion, which explores the ways in which barrier morphologic evolution influences the rate and pattern of shrub expansion. To account for storm stochasticity in the model, each unique combination of parameter values was simulated 100 times. Simulations run for 1,000 model years for the Barrier_evolution experiment, and 150 years for the Shrub_expansion experiment, or until the barrier drowns. The location of the initial cluster of shrub establishment is randomly determined in the Barrier_evolution experiment, but is constrained to the first 100 m (2%) alongshore in the Shrub_expansion experiment. Results from the two experiments are contained in two different files: barrier_island_evolution.csv and shrub_expansion.csv. A table of parameter values, definitions, and sources for the shrub module used in the experiments is given in the file: parameter_values_sources.csv
Data set for initializing and forcing of high-resolution local area model implementations in two ATLAS case study areas, Rockall Bank and Condor Seamount
<p>The data set includes all necessary data for set up, initial and boundary conditions of high-resolution local area model implementations using the ROMS-AGRIF model in two case study areas, Rockall Bank and Condor Seamount, conducted as part of the EU ATLAS project. The data include all computational grids, initialization fields (temperature, salinity) and boundary conditions (temperature salinity, currents, sea surface height) for each case study area.</p>
Data supplement for Wind Energy Science Paper 'Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact'
<p>Contains the data for most figures in the article, as well as a plotting file written in python that generates the figures.</p>
Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"
<p>These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions.<br> The data are used in the manuscript entitled "Reconciling compensating errors between<br> precipitation constraints and the energy budget in a climate model".</p>
Air quality Modelling data for Guildford City
<p>The simulation data for Guildford City using ADMS-Urban at 1 km spatial resolution. </p>
3D Cortical Bone and Trabecular Bone Structure [synthetic data, simple, capsule shell model]
<p>Trabecular bone patterns are mimicked by generating and arranging "capsule shells" in a three-dimensional voxel by following probability distribution. Ground truth (gt) contains 4 labels (Background: 0, Cortical Bone: 11, Trabecular Bone: 21, Cavity: 31).</p>
Supplementary data for 'Results of the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)'
<p>Datasets and model datasheets provided to the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)</p>
High-CAPE summer convection in large-domain large-eddy simulations with ICON - model and observational data sets
<p>Data sets including all observational and ICON model data for publication in Atmosperic Chemistry and Physics Journal (ACP) - "High-CAPE summer convection in large-domain large-eddy simulations with ICON"</p>
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.1
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.1 [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS-v.2.1 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS.2.1_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p> </p> <p>References:</p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS outputs (from four different Land Surface Models) are available in 1º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS_<strong><em>mod</em></strong>10_3H_<strong><em>yyyy</em></strong>.tar.gz (Note: <em><strong>mod</strong></em> = CLM or MOS or NOAH or VIC; <strong><em>yyyy</em></strong> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p> </p> <p>References:</p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
JUMP - Data collection - Part I: Jets from a Global Climate Model.
<p>We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes:</p> <p>Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-Icarus.pdf</strong> that describes in depththe data set and the associated nomenclature.</li> <li>A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> <li><strong>StatisticalData.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry are on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> </ul> <p> </p> <p><strong>Acknowledgments</strong>:</p> <p>The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper.</p>
Simulation data for Modelling DNA-strand displacement reactions in the presence of base-pair mismatches
<p>Raw and processed simulation data from the paper Modelling DNA-strand displacement reactions in the presence of base-pair mismatches (J. Am. Chem. Soc.)</p>
Arsenic adsorption modelling: TiO2, Fe2O3 and composite TiO2-Fe2O3 sorbents - detailed characterisation and adsorption data sets
<p>Raw data, example files and templates for the research paper provisionally titled 'Improved Accuracy in the Surface Complexation Modelling of Arsenic on Multicomponent Sorbents Using Low Energy Ion Scattering'</p> <p>Included is</p> <ul> <li>materials characterisation data (TiO2, Fe2O3 and a TiO2-Fe2O3 composite)</li> <li>arsenic(III) and arsenic(V) adsorption data (both adsorption isotherms and pH adsorption edges)</li> <li>potentiometric titration data</li> <li>templates for preparing FITEQL input files from titration data and pH adsorption edges</li> <li>examples of FITEQL input and output files</li> </ul> <p>Characterisation techniques used include BET, XRD, FTIR, zeta potential, DLS, low energy ion scattering (LEIS), XPS and XRF. Data was primarily collected by Jay Bullen between 2016 and 2019, with assistance from named collaborators.</p>
Data set to ''Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling'
<p>This data set is the supplementary material to Grosse et al. (2020) 'Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling', published in Tectonophysics (https://doi.org/10.1016/j.tecto.2020.228411). The data set consists of (1) 249 digital elevation models (DEMs) of each step of the the analogue experiments carried out, in standard ENVI format, zipped; and (2) an Excel file containing the DEM-derived morphometric parameters for each of the analogue models.</p> <p>Experiments were carried out at the analogue modelling lab of the Department of Geography at the Vrije Universiteit Brussel (Belgium). A granular mixture of fine-grained quartz sand and kaolin clay was used as analogue material. Experiments were conducted on a fixed table, on which a basal layer of granular material was placed. A basal plate attached to a step-motor was used to simulate pure strike-slip displacements of the basal layer. Volcano growth was simulated by depositing loads of granular material on top of the basal layer from a point source. The analogue models were photographed at regular time intervals during the experiments using four digital cameras. The photographs were used to generate synthetic digital elevation models (DEMs) with 0.2 mm spatial resolution of each step of the analogue models by applying the MICMAC digital stereo-photogrammetry software. The ENVI software was used to re-sample the DEMs to a 0.5 mm spatial resolution and apply the noise-reduction Lee filter. Morphometric data were then extracted from the DEMs by applying two IDL-language algorithms: NETVOLC, used to automatically calculate the volcano edifice basal outline, and MORVOLC, used to extract a set of morphometric parameters.</p>
Model data for "Flow Separation and Increased Drag Coefficient in Estuarine Channels with Curvature"
<p>These are the model data we generated using ROMS and analyzed for the journal article "Increased Drag Coefficient in Estuarine Channels with Curvature". Files include 8 sinuous channel models and 2 straight channels. sinuous_channel_1.nc and straight_channel_1.nc are the pair of models analyzed in section 3. sinuous_channel_1_avg.nc and straight_channel_1_avg.nc are the one-hour average result. sinuous_channel_2_avg.nc and straight_channel_2_avg.nc are the pair of deep channel models. sinuous_channel_3_avg.nc and others are the other different sinuous channel models.</p>
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>
Supporting model data for Paleogeographic controls on the evolution of Late Cretaceous ocean circulation by Ladant, J.-B., et al. in Climate of the Past, doi:10.5194/cp-2019-157.
<p>The dataset is comprised of CCSM4 model variables required to reproduce the figures shown in the following manuscript:</p> <p>Ladant, J.-B., C. J. Poulsen, F. Fluteau, C. R. Tabor, K. G. MacLeod, E. E. Martin, S. J. Haynes and M. A. Rostami, Paleogeographic controls on the evolution of Late Cretaceous ocean circulation, Climate of the Past, doi:10.5194/cp-2019-157.</p>
Raw data to "Blunting neuroinflammation with resolvin D1 prevents early pathology in a rat model of Parkinson's disease"
<p>Background: In vivo treatment of animals with anti-inflammatory and pro-resolving mediators (SPMs) could be counteracted by their limited in vivo bioavailability due to their unstable nature as lipids that can undergo oxidation or enzymatic degradation.</p> <p>Results: Thus, we performed a time course of RvD1 plasma levels over 36 hours after an initial intraperitonael injection of this lipid mediator at a concentration of 200 ng/animal. After a single injection the plasma concentration of RvD1 peaked at 1h (~360 pg/ml), stayed almost constant at 3h(~360 pg/ml), halved its levels after 6h (~180 pg/ml) and slowly returned close to the baseline after 36h (~30 pg/ml). These results indicate that RvD1 is rapidly distributed into the bloodstream and eliminated from the vascular compartment due to metabolism and/or diffusion into the blood–brain barrier.</p> <p>Conclusions: These findings are important to define route and timing of in vivo administration of SPMs in order to maintain sufficient levels to sustain biological activities both in the periphery and within the central nervous system.</p>
Fig. 1 in Data publication and dissemination of interactive keys under the open access model
Fig. 1. Th e ZooKeys model for data publication and dissemination of interactive keys.
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.