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695 results for “model output”
Model Output for Eddy Mediated Mixing in the Equatorial Pacific
<p>This repository contains processed model outputs used in the "Eddy Mediated Mixing in the Equatorial Pacific" submitted manuscript. </p>
Terrestrial laser scanning data of urban trees in Milton Keynes, UK: individual trees and Treegraph model outputs
<p><strong>TLS_point_clouds:</strong></p> <p>- Data collection: Terrestrial laser scanning data acquired in leaf-off condition in April 2021.</p> <p>- Scanning instrument: We used a RIEGL VZ-400 with a wavelength of 1550 nm, 0.35 mrad beam divergence and 0.04˚ angular resolution.</p> <p>- Locations: The data were collected from three urban sites in Milton Keynes, UK: Avebury Blvd (Ave), Dansteed Way (Dan), and Overgate (Ove).</p> <p>- File information: Each file is an individual tree point cloud in Polygon File Format (.ply), which can be viewed in software such as CloudCompare.</p> <p> </p> <p><strong>Treegraph_outputs:</strong></p> <p>- Description: Model outputs from <em>Treegraph</em> for individual trees.</p> <p>- File naming convention: [TreeID]-[downsample_voxel_length]-[tip_diameter_if_known].*</p> <ul> <li>*.centres.ply: Skeleton nodes of individual trees.</li> <li>*.mesh.ply: Cylinder model of individual trees.</li> <li>*.json: Geometrical and topological attributes at varying scales, from internode and branch to the whole tree.</li> <li>*.txt: Summary of input parameters, logs of intermediate steps, and a statistical overview of whole-tree structural attributes.</li> </ul>
A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017
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Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event
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Model output tracking smoke from agricultural fires in south Florida from October 2022 - May 2023
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A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018
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Community Land Model synthetic meteorology simulation model output
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Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
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Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska
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Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
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Model executable, output, drivers and parameters for modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources
Files used to generate the data for figures in: Rastetter, EB, Kwiatkowski, BL. An approach to modeling resource optimization for substitutable and interdependent resources. Ecological Modelling (2020). https://doi.org/10.1016/j.ecolmodel.2020.109033. This paper presents a hierarchical approach to modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources. Substitutable resources are resources that fill the same metabolic or stoichiometric need of the organism. Interdependent resources are resources whose acquisition or expenditure are tightly linked (e.g., light, carbon dioxide, and water in photosynthesis and associated transpiration). We illustrate the approach by simulating the development of vegetation with four substitutable sources of nitrogen that differ only in the cost of their uptake and assimilation.
Model output dataset used in "Assessment of Ch in the Noah-MP LSM over China"
<p>1. The Noah-MP LSM v3.6 was used in an offline standalone mode to execute single-site and regional-scale land surface experiments. Single-point experiments were executed at nine flux tower sites, while the simulated regional domain covered all of China.</p> <p>2. Three cases were designed: (1) the original M-O option with identical <em>Z<sub>om</sub></em> and <em>Z<sub>ot</sub></em> (Default), (2) a constant <em>C<sub>zil</sub></em> specified as 0.1 (Czil), and (3) a dynamic canopy-height-dependent <em>C<sub>zil</sub> </em>( Newczil).</p> <p>3. The model output data files for single-site land surface experiements are named in accordance with the following convention:</p> <p>SiteName_SchemeName_start{year/month/day/hour/minute}_end{year/month/day/hour/minute}UTC_noahmp.nc</p> <p>For regional-scale land surface experiments (UTC):</p> <p>NoahMP.{year/month/day}T{hour}0000.nc</p> <p>The file "Domain_regional-scale.nc" contains the static geographic information, such as land cover.</p>
RAPID Model Output Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins
<p>This database contains the simulated output files of the RAPID model (river flow in m<sup>3</sup>/s); which 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. If you use this RAPID Model Output Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: <em>Sikder et al.</em>, [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> <p>The database contains;</p> <p>River Flow Files: Qout_<strong><em>res</em></strong>_MIGBM_20000101_20091231_<strong><em> prj</em></strong>_<strong><em>LSMsr</em></strong>_<strong><em>tr</em></strong>_utc.nc</p> <p>(Note: <strong><em>res</em></strong> = fine or coarse; <strong><em>prj</em></strong> = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; <strong><em>LSM</em></strong> = CLM, MOS, NOAH, VIC, ERAint; <strong><em>sr</em></strong> = 10 or 025; <strong><em>tr</em></strong> = 3H or D)</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>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></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>
GEOS-Chem v9-02 with simple SOA scheme for ATAL simulations & model output
<p>This code/data repository includes (1) GEOS-Chem (v9-02, <a href="http://www.geos-chem.org/">http://www.geos-chem.org/</a>) code modified to use the simple SOA scheme in the simulation of Asian Tropopause Aerosol Layer (ATAL), and (2) model ATAL simulation output. Please see README.txt and this paper for details:</p> <p>Citation: Fairlie, T.D., H. Liu, J.-P. Vernier, P. Campuzano-Jost, J. L. Jimenez, D.S. Jo, B. Zhang, M. Natarajan, M.A. Avery, and G. Huey, Estimates of regional source contributions to the Asian Tropopause Aerosol Layer using a chemical transport model, Journal of Geophysical Research-Atmospheres, in press, Jan. 2020.</p>
Model outputs: Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations
<p>This dataset contains six model outputs of vegetation biomass on plant functional type level and total vegetation biomass on grid cell with and without bias-correction in different sensitivity experiments, as described in Readme.txt.</p>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
Flow of Agricultural Nitrogen, version 2 (FANv2): Model input and output data
<p>This upload includes data associated with the manuscript "An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen, version 2 (FANv2)" submitted to Geoscientific Model Development. The dataset includes an input file for use with the Community Land Model, and an output file with the simulated ammonia emissions for the agricultural sector. The emissions are monthly averages from the simulation for 2010-2015. Additional information is given in the readme file.</p>
CAS FGOALS-f3-L model output
<p>The file includes the model output of the CAS FGOALS-f3-L which associated with the assessing of the aerosol effective radiative forcing.</p>
Clouds and Radiation in a mock-Walker Circulation model output
<p>Data used in the creation of figures for Silvers and Robinson, Clouds and Radiation in a mock-Walker Circulation. 2020, JAMES. </p> <p>These experiments are described in detail in the paper. They use the AM4.0 physics package on a doubly periodic domain with a warm patch of SST that is fixed in time at the center of the domain. Grid-Spacings of 1km, 2km, 25km, and 100km are explored on different domain sizes. The data set focuses on analysis of the atmospheric circulation and cloud response to this SST warm region.</p> <p>For questions please contact Levi Silvers at levi.silvers@stonybrook.edu, thomas.robinson@noaa.gov</p> <p>Directory contents are described below:</p> <p>#=========================================================================================<br> # data for the P100, P100L, P25, P25L, E25 experiments are in the following directories <br> #=========================================================================================<br> # P100<br> c8x40L33_am4p0_100km_wlkr_ent0p9<br> c8x40L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P100L<br> c8x160L33_am4p0_100km_wlkr_ent0p9<br> c8x160L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P25<br> c8x160L33_am4p0_25km_wlkr_ent0p9<br> c8x160L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # P25L<br> c8x640L33_am4p0_25km_wlkr_ent0p9<br> c8x640L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # E25<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv_lwoff</p> <p># within the directories for each of these experiments: P100, P100L, P25, P25L, E25<br> # there are two files: 1979th1983_daily.nc and 1980th1983.atmos_month_tmn.nc</p> <p># within each of these files are the following variables: <br> 1979th1983_daily.nc<br> 'prec_conv'<br> 'prec_ls'<br> 'precip'</p> <p>1980th1983.atmos_month_tmn.nc<br> 'ps'<br> 'temp'<br> 'ucomp'<br> 'w'<br> 'sphum'<br> 'cld_amt'<br> 'z_full'<br> 'precip'<br> 'prec_conv'<br> 'prec_ls'<br> 'rh'<br> 'tot_liq_amt'<br> 'tot_ice_amt'<br> 'tdt_lw'<br> 'evap'<br> 'shflx'</p> <p><br> #=========================================================================================<br> # data for the E1 and E2 experiments are in the following directories <br> #=========================================================================================<br> # E1 <br> c10x4000L33_am4p0_1km_wlkr_4K<br> c10x4000L33_am4p0_1km_wlkr_4K_lwoff<br> # E2 <br> c50x2000L33_am4p0_2km_wlkr_4K<br> c50x2000L33_am4p0_2km_wlkr_4K_lwoff</p> <p># within each of these directories are two files: 1979_6mn.atmos_month.nc and 1979.6mn.atmos_daily_selvars.nc<br> # these files contain the following variables: </p> <p># 1979_6mn.atmos_month.nc<br> tdt_lw<br> t_surf<br> evap<br> shflx<br> tot_ice_amt<br> tot_liq_amt<br> rh<br> precip<br> z_full<br> cld_amt<br> sphum<br> ucomp<br> temp <br> w<br> ps<br> shflx</p> <p># 1979.6mn.atmos_daily_selvars.nc<br> t_surf<br> ps<br> precip</p> <p>Acknowledgment: Aparna Radhakrishnan supported the provision of this Zenodo DOI. <br> </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.