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695 results for “model output”
Model output for Chester et al. (2024): On the origin of Holocene sea-level transgressions in formerly glaciated regions
<p>Model output files and post-processing/plotting scripts for Chester et al. (2024, QSR). </p> <p>This repository contains:</p> <p>Files for reproducing figures:</p> <ol> <li> hingeline_tracker_zenodo.m. This code takes GIA model output and identifies the hinge line for a given transect following Chester et al. (2024) (e.g. Figures 4-6). </li> <li> transgression_magnitude_id_loop_zenodo.m. This code takes GIA model output and identifies and magnitude of a transgression and timing of the highstand for a given region (e.g. Figures 9, 10 and 12 in Chester et al., 2024). </li> </ol> <p>Zip files with GIA model output in .mat form:</p> <ol> <li>LN-SL_ICE7G_100km_plusBestFitting.zip: output from the LN-SL model with the ICE7G reconstruction, a 100 km lithosphere, and the mantle viscosities indicated in Table 1. The folder also includes the output from the two best-fitting simulations used to create Figures 9 and 10 in Chester et al. (2024). </li> <li>LN-NO_ICE7G_100km.zip: output from the LN-NO model with the ICE7G reconstruction, a 100 km lithosphere and the mantle viscosities indicated in Table 1. </li> <li>FN-NO_discload.zip: output from simulations with the FN-NO model. The model was run with the disc load ice sheet and the Earth structures indicated in Table 1. </li> </ol> <p>For GIA model runs with different parameter combinations please contact me at schester@ldeo.columbia.edu. </p> <p>Note on file labeling: 'rsl_ice7g.l60CC.ump1.lm10.mat' translates to, ice model: ICE7G, lithospheric thickness: 60 km, upper mantle viscosity: 0.1 * 10^21 Pa s, lower mantle viscosity: 10 * 10^21 Pa s. </p> <p> </p> <p> </p>
Rendered HOTSSea model output for the pacea R package
<p>This files are for the pacea R package https://github.com/pbs-assess/pacea and are automatically downloaded to your local machine when running the function pacea::hotssea_all_variables() . See the hotssea vignette for details. </p> <p>The Hindcast of the Salish Sea (HOTSSea) is a physical ocean model that recreates conditions throughout the Salish Sea from 1980 to 2018, filling in the gaps in patchy measurements. See pacea for reference. </p>
MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier"
<p>MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier</p> <p>Here, it contains the results of the East Antarctic simulation from 1992-2016. Model grid is lat-lon similar to LLC1080 grid resolution roughly 3-4 km in the region. See Nakayama et al., submitted to GRL for detail. </p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> results_zenodo.zip (due to size limit of 50GB, please check <a href="https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antartic">https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antarctic</a> for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..<br> mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx/ .<br> cp ../build/mitgcm_uv .<br> qsub run_omp_high_t1.pbs</p>
ED2-hydro model output from simulations at Barro Colorado Island
<p>Monthly model output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009; Powell et al., 2018) run at Barro Colorado Island (BCI). ED2 was initialized from bare ground at BCI and run with recycled 2008-2014 observed meteorology (i.e. “BASE”) until predictions of above ground biomass (AGB) reached dynamic equilibrium after 700 years (Powell et al., 2018; referred to as simulation year 0 in the simulations presented here). The four data sets provided here all start after the spin up period and each provide a 50 year (monthly time step) time series of selected ED2 history variables under a particular precipitation scenario: 1) a synthetic El Niño time series (Powell <em>et al.</em>, 2017) which produces two exceptionally strong El Niño events within 30 years (the “ENSO” scenario), 2) a “WET” scenario where precipitation increased 30% compared to BASE precipitation, and 3) a “DRY-DS” scenario where dry season (January–April) precipitation was reduced 75% compared to BASE and 4) a continuation of BASE meteorology.</p> <p><strong><strong>Medvigy D</strong>, <strong>Wofsy SC</strong>, <strong>Munger JW</strong>, <strong>Hollinger DY</strong>, <strong>Moorcroft PR</strong></strong>. <strong>2009</strong>. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. <em>Journal of Geophysical Research: Biogeosciences</em> <strong>114</strong>: 1–21.</p> <p><strong><strong><strong>Powell TL</strong>, <strong>Koven CD</strong>, <strong>Johnson DJ</strong>, <strong>Faybishenko B</strong>, <strong>Fisher RA</strong>, <strong>Knox RG</strong>, <strong>McDowell NG</strong>, <strong>Condit R</strong>, <strong>Hubbell SP</strong>, <strong>Wright SJ</strong>, <em>et al.</em></strong></strong> <strong>2018</strong>. Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity. <em>New Phytologist</em> <strong>219</strong>: 932–946.</p> <p><strong><strong>Powell T</strong>, <strong>Kueppers L</strong>, <strong>Paton S</strong></strong>. <strong>2017</strong>. Seven years (2008-2014) of meteorological observations plus a synthetic El Nino drought for BCI Panama.</p> <p> </p> <p> </p>
Databases for "A multiscale approach to identify key factors determining rural WaSH service level and sustainability" (ShinyApp). Application to SIASAR conceptual model v1, outputs obtained from Nicaragua, Honduras, Panama and Dominican Republic SIASAR databases (last accessed: December 21, 2016)".
<p>Attached databases contain the results obtained by applying the SIASAR composite indicators v1 (available at http://upcommons.upc.edu/handle/2117/77587) to data presented in http://doi.org/10.5281/zenodo.571351</p> <p>These files can be directly exploited by using the ShinyApp “A multiscale approach to identify key factors determining rural WaSH service level and sustainability”.</p> <p>Each *.csv file contains one country's database. </p>
The effect of past saturation changes on noble gas reconstructions of mean ocean temperature: Model Output Data
<p>This dataset contains model output for the simulations presented in "<em>The effect of past saturation changes on noble gas reconstructions of mean ocean temperature</em>".</p> <p>The NetCDF4 files contain the following variables:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>Sea-ice area</li> <li>Ar, Kr, Xe, and N<sub>2</sub> concentrations</li> <li>AMOC strength and Northern-Sourced Water fraction (only transient runs)</li> </ul>
model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer
<p><strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. </p> <ul> <li>2s_het: main simulation with two-stream solver</li> <li>2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes</li> <li>2s_hom_hrsw: two-stream solver, horizontally averaged heating rates</li> <li>rt_het: main simulation with ray tracing, 256 samples per pixel </li> <li>rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel </li> <li>rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel </li> <li>rt_het_128: ray tracing, 128 samples per pixel</li> <li>rt_het:_64 ray tracing, 64 samples per pixel</li> <li>rt_het:_32 ray tracing, 32 samples per pixel</li> </ul>
Processed model output used in 'The impact of winds on AMOC in a fully-coupled climate model'
<p>Processed model output from wind-nudging experiments used to investigate the Atlantic Meridional Overturning Circulation. </p> <p> </p> <p>For further details, see </p> <p>Roach, L. A, Blanchard-Wrigglesworth E. Ragen, S., Cheng, W., Armour, K. and Bitz, C. M.. (2022). The impact of winds on AMOC in a fully-coupled climate model. In review at Geophysical Research Letters</p>
Results and assumptions for: Modeling the Circular Economy in Environmentally Extended Input-Output Tables: methods, software and case study
<p>This dataset presents supplementary information for <em>"Modeling the Circular Economy in Environmentally Extended Input-Output Tables: methods, software and case study" </em><a href="https://doi.org/10.1016/j.resconrec.2019.104508">https://doi.org/10.1016/j.resconrec.2019.104508</a></p> <p>The data was processed and results were obtained through https://cmlplatform.github.io/pycirk/</p> <p> </p> <p><br> Annex I: Scenario assumptions and modeling choices and complete results (file Annex_I.xlsx) <br> Annex II: Contains analysis of other software, database modifications, and list of affected categories (file Annex_II.docx) <br> Results: settings, assumptions, results and their analysis from the case study presented in the paper (file Donati_CE_EEIO_Mo_SI.tar.gz)</p>
Bayesian models' outputs for: A new method to explicitly estimate the shift of optimum along gradients in multispecies studies
<p>This repository contains data to reproduce analysis presented in the paper:</p> <p>B. Mourguiart, B. Liquet, K. Mengersen, T. Couturier, J. Mansons, Y. Braud, A. Besnard . A new method to explicitly estimate the shift of optimum along gradients in multispecies studies. <em>Journal of Biogeography</em>, (in press).</p> <p>The paper introduces a new formulation of a Bayesian hierarchical linear model that explicitly estimates optimum shifts for multiple species having symmetrical response curves. This new formulation, called Explicit Hierarchical Model of Optimum Shifts (EHMOS), is compared to a mean comparison method and a Bayesian generalized linear mixed model (GLMM) using simulated and real datasets. Fitting the models to the simulated data took several days. Here we provide the models' outputs needed to reproduce the results presented in the paper without re-running the models. </p>
Abeshu-etal_2023_GMD: Model Input and Output data
<p>1. Input data required to conduct the experiment</p> <p>2. Model output data for reproducing the manuscript figures</p>
In- and output files for krill growth models intercomparison (Bahlburg et al. 2023)
<p>These datasets are required to re-run the krill growth model simulations presented in Bahlburg et al. (2023)</p> <p>The code repository can be found under https://github.com/dbahlburg/krillGrowthModelsComparison</p> <p>The ZIP file contains two folders: One containing the input data including the climatologies which should be deposited into inputData/climatologies in the project</p> <p>The other folder contains the simulation results which should be copied into the "simulationResults"-folder in order to run the "10ManuscriptPlots.R"-Script.</p>
Model output for "Catchment coevolution and the geomorphic origins of variable source area hydrology"
<p>Model output supporting "Catchment coevolution and the geomorphic origins of variable source area hydrology" for submission to Water Resources Research. The Python package DupuitLEM v1.1-alpha (DOI: 10.5281/zenodo.7620978) contains the models and scripts used to generate and post-process output.</p>
Model output publication 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'
<p>The model output for the publication in JGR: Oceans titled 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'</p>
Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".
<p>This archive contains post-processed data for analyses in Qian et al. (2023) "<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>". These data are model output from the Perturbed Parameters Ensemble (PPE) simulations conducted with DOE's E3SM Atmosphere Model Version 1 (EAMv1). There are 256x12 PPE simulations with each simulation run for 5 days following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper. </p>
Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"
<p>Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"<br> Submitted to Global Biogeochemical Cycles<br> Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz</p> <p>This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper.<br> Model outputs are also provided, so there is no need to run the entire model to generate the figures.</p> <p>The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:<br> - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.<br> - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.<br> - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10.</p> <p>The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html </p> <p>These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study.</p> <p>The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, https://doi.org/10.17882/42182). The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021).</p> <p>The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0</p> <p> </p>
Everest Model Output Statistics: improved wind speed forecasts
<p>Data and pre-trained random forest models necessary to correct GFS forecast data and produce improved Everest Forecasts.</p> <p>Please find the associated code at: github.com/MaxVWDV/Everest_wind_forecast</p>
Preprocessing output for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Outputs of the local preprocessing of the OpenEO data (see <a href="https://doi.org/10.5281/zenodo.7691342">here</a>) and pywaterinfo data (see <a href="https://doi.org/10.5281/zenodo.7689200">here</a>).</p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p>
Output from SHUD hydrological model for Waerma Watershed, Gansu, China
<p>The Waerma watershed is a headwater of the Yellow River, with an area of 9.8 $km^2$, located near Waerma Village, about 20 kilometers northwest of Maqu County in Gansu province of China. </p> <p> </p>
Model output from Wilson et al paper
<p>Model output used in the publication by N. J. Wilson, C. A. Vreugdenhil, B. Gayen, E W. Hester (submitted for review). </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.