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695 results for “model output”
Source code and model outputs for 'Increasing Aerosol Direct Effect Despite Declining Global Emissions'
<p>Source code, model outputs and python scripts used for the publication of Hermant, A., Huusko, L., & Mauritsen, T: Increasing Aerosol Direct Effect Despite Declining Global Emissions</p>
Diablo model output for LES basal slope effects study
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Main MARINA-Nutrients model outputs for European river basins for the period of 2017-2020
<p>The excel file "main_MARINA-Nutrients_model_outputs_for_Europe_2017-2020" contains main results of the publication (https://doi.org/10.15302/J-FASE-2023526). The model results are presented in unit of load (kg/yr) for the period of 2017-2020 per river basin. These results are obtained by model calculations following the equations that are provided in the "Materials and Methods" of the publication and in the supporting file. "Codebook.csv" file includes more details on the data.</p>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Supplementary material S5: Model outputs
<p>Supplementary material S5 to</p> <p>Schmid, U., Frehner, M., Bugmann, H., 2024. Management recommendations for Alpine protection forests: the importance of regeneration quality and initial stand composition.</p>
Light of History: Model weights and classified output
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Simulation outputs associated with Maffre et al. "GEOCLIM7, an Earth System Model for multi-million years evolution of the geochemical cycles and climate." (submitted to GMD)
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Model code and output for "Transformation Processes in the Oder Lagoon as seen from a Model Perspective"
<p>This dataset contains the numerical model output files that were used in the analysis, the numerical model input files that were used to produce the model simulation, and the specific source code for the model components presented in "Transformation Processes in the Oder Lagoon as seen from a Model Perspective", submitted to Biogeoscience.</p> <p>data1.tar is the direct diagnostic model output. data2.tar are condensed data which are used for the analysis.</p> <p>forcing.tar contains data to run the model. Most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time (1995) is provided for the first year of the simulation. This dataset does not include the coastdat atmospheric data, which can be downloaded directly from https://www.wdc-climate.de/ui/entry?acronym=coastDat-2_COSMO-CLM.</p> <p>src.tar contains the model source code.</p>
Model output (ROMS_HH2) of simulating OAE in Halifax Harbour
<p><span>This repository provides model output (HRM2) for analysis in the following article:</span></p> <p><span>Wang B., Laurent A., Pei Q., Sheng J., Atamanchuk D., Fennel K. Maximizing the detectability of Ocean Alkalinity Enhancement (OAE) while minimizing its exposure risks: Insights from a numerical study. preprint</span></p>
Model output (ROMS_HH3) of simulating OAE in Halifax Harbour
<p>This repository provides model output (HRM3) for analysis in the following article:</p> <p>Wang B., Laurent A., Pei Q., Sheng J., Atamanchuk D., Fennel K. Maximizing the detectability of Ocean Alkalinity Enhancement (OAE) while minimizing its exposure risks: Insights from a numerical study. preprint</p>
Small model output dataset for teaching use
<p>Small model output dataset from CAM6-Nor for teaching use.</p>
Model output for "Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes"
<p>Model output supporting "Groundwater affects the geomorphic and hydrologic properties of coevolved landscapes" in JGR Earth Surface, DOI:10.1029/2021JF006239. The Python package DupuitLEM v1.0-beta (DOI:10.5281/zenodo.5522828) contains the models and scripts used to generate and post-process output.</p>
Inputs and outputs for bilayers simulations in "Accurate Simulations of Lipid Monolayers Require a Water Model With Correct Surface Tension"
<p>Inputs and outputs file for simulations of POPC and DPPC Bilayers at various temperatures. For details see:</p>
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Sea Ice Rheology Experiment (SIREx) - Model output data
<p>Sea-ice model output analyzed in the Sea Ice Rheology Experiment (SIREx) Part I and Part II. </p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Each netCDF file contains daily output (as means or instantaneous values -- as indicated in the name) for January-February-March of the given year. Please see below for more information on what is included and how to cite. </p> <p> </p> <p><strong>1. Naming convention of the files</strong></p> <p>"Model simulation label" + _ + "year" + _ + "daily_means" OR "instant" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>U,V</em> : sea-ice velocity in the x,y directions (in m/s);</li> <li><em>A</em>: ice concentration per grid cell (0 -- 1);</li> <li><em>h</em>: mean ice thickness per grid cell (in m);</li> <li>Grid spacing information: centered on u-points (<em>DXU, DYU</em>), v-points (<em>DXV, DYV</em>), or t-points (<em>DXT, DYT</em>) (in m); </li> <li>Grid point coordinates: for u-points (<em>ULON, ULAT</em>), v-points (<em>VLON, VLA</em>T), or t-points (<em>TLON, TLAT</em>) (in degrees); </li> <li><em>maskT</em>: land mask at t-points (0/1 = ocean/land);</li> <li><em>time</em>: time axis (in days since 1901-01-01 00:00:00).</li> </ul> <p> </p> <p><strong>3. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx papers (Bouchat et al., 2022, Hutter et al., 2022 ). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
HYSPLIT ensemble model output for case studies
<p>Each folder named like a date (YYYYmmddHHMM) has a case study and it's named after eruption start time.</p> <p> * <strong>201208061200 </strong>- Historical case study for eruption starting at 2012-08-06T12:00 UTC<br> * <strong>202107150000</strong> - Operational case study for eruption starting at 2021-07-15T00:00 UTC</p> <p>Each case study folder has :<br> - <strong>input</strong> folder with the eruption source parameters, sampled with Latin Hyspercube technique, used to configure each HYSPLIT run.<br> - <strong>output </strong>folder with the HYSPLIT modelling results processed to cumulative deposited ash thickness netcdf file.</p> <p>Code to process results is in this <a href="http://github.com/yannikbehr/ashfall_forecasts/">Git repository</a></p> <p> </p>
United States Environmentally-Extended Input-Output (USEEIO) Modeling Framework
<p>This is a snapshot of the <a href="https://github.com/USEPA/useeio">USEEIO modeling framework</a> repository that is compilation of a README, versioning scheme, and a python script supplemented with a to convert a USEEIO model generated from <a href="https://github.com/USEPA/useeior">useeior </a>in the API format into openLCA schema JSON-LD files. This is not source code for generating the USEEIO v2.0 model.</p>
MITgcm model setup and output for "What determines the shape of the Pine-Island-like ice shelf?"
<p><strong>(Contents)</strong><br> Here it contains<br> <br> jim_run3 = CTRL (Similar to Jordan et al., 2018 but with smaller ocean)</p> <p>melt/run/ = IOCTRL, M(all)V(dyn)U(dyn)</p> <p>melt2/ = M( changing, see below ) V( dyn ) U( 0 )<br> melt2/run/ = M(all)V(dyn)U(0)<br> melt2/run2/ = M(20)V(dyn)U(0)<br> melt2/run3/ = M(GL10)V(dyn)U(0)<br> melt2/run4/ = M(GL20)V(dyn)U(0)</p> <p><br> melt3/ = M ( changing, see below ) V( changing ) U( 0 )<br> melt3/run5/ = M(all)V(2000)U(0)<br> melt3/run10/ = M(20)V(2000)U(0)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> Input melt data for running ice only experiments<br> #data.streamice#<br> meltCTRL.bin = M(all) ... melt rate data of CTRL for every time steps <br> meltCTRL2.bin = M(GL10)<br> meltCTRL3.bin = M(20 m/a)<br> meltCTRL4.bin = M(GL20)</p> <p>Input velocity data for running ice only experiments <br> melt3/<br> uvel_ext4.bin = U(0 m/a)<br> vvel_ext5.bin = V(2000 m/a)</p> <p><strong>(How to compile and run)</strong><br> mkdir build<br> cd build/<br> module load intel/2021.2.0<br> module load impi/2021.2.0<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> ../../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> make -j 16</p> <p> mkdir ../run<br> cd ../run/<br> cp ../build/mitgcmuv .<br> cp ../input/* .<br> pjsub job*.pbs<br> <br> <strong>Other important links</strong><br> https://github.com/hgu784/MITgcm_67s<br> <br> For more info please send an email to Yoshihiro.Nakayama@lowtem.hokudai.ac.jp</p>
Model outputs and observation data for "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)"
<p>Model outputs and observation data for paper "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)".</p>
Selected HRRRv4 model output and plotting scripts for 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model'
<p>This zip file contains selected model output data from HRRRv4 and plotting scripts used for the paper 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model' which is submitted for publication to the journal Geoscientific Model Development (GMD).</p> <p>The individual files are:</p> <p>d01_d02_lwd_allsurfacestations_v4fp1_v4fp2.nc: longwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_Wasco_v4fp1_v4fp2.nc: liquid water path at Wasco for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_allsurfacestations_v4fp1_v4fp2.nc: liquid water path at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_gridpointsbelow500mMSL_v4fp1_v4fp2.nc: liquid water path at all grid points in the Columbia River Basin with a terrain height of less than 500 m for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_numbergridpointsbelow500mMSL_v4fp1_v4fp2.nc: Number of gridpoints in the Columbia River Basin with a terrain height of less than 500 m MSL and a LWP larger than 10 g/m2 for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_swd_allsurfacestations_v4fp1_v4fp2.nc: shortwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_t2m_allsurfacestations_v4fp1_v4fp2.nc: 2-m temperature at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_windspeed_7sites_v4fp1_v4fp2.nc: Wind speed profiles at 7 sites in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d01 and run v4fp1<br> d01_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d01 and run v4fp2<br> d02_swd_spatial_v4fp1.nc: spatial distribution of surface shortwave downward radiation for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d02 and run v4fp2<br> d02_terrain.nc: terrain height for domain d02<br> plot_miscs_hrrr_longforecasts.py: master script for plotting<br> plot_routines_wfip2_lf.py: plot routines for various type of plots<br> read_routines_wfip2_lf.py: read routines for all kinds of data<br> sites_locations_wfip2.txt: latitude, longitude and height of used stations<br> basic_functions.py: helper functions for plotting</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.