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136 results for “Model Versioning”
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation
<p>The model code and necessary data: NESMv3_gmd.tar.gz.</p> <p>The model manual :Using NESM v3 model.pdf</p> <p>The reference: Reference.tar.gz</p>
santis19/paper-plate-contraction-patagonia: Plate Boundaries Model Version 1.2
<p>Supplementary material for paper <em>Transient plate contraction between two simultaneous slab windows: Insights from Paleogene tectonics of the Patagonian Andes</em>, including new plate boundaries model.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
MICOM model databases version 1
This repository includes pre-built model databases for the microbial community modeling tool MICOM (https://micom-dev.github.io/micom). All model databases will work with the MICOM python package or the MICOM Qiime 2 plugin (http://github.com/micom-dev/q2-micom).<br><br> The Zenodo release is built and versioned automatically from the the source repository at http://github.com/micom-dev/databases. Thus, all bug reports, requests, and comments should be made there.<br> Model databases are named with the following scheme:<br> SOURCE_SVER_TAXONOMY_RANK_VERSION.qza<br> where:<br> <ul> <li>SOURCE = source database for the models</li> <li>SVER = version of the source database</li> <li>TAXONOMY = the taxonomy naming scheme used</li> <li>RANK = the taxonomic rank models were collapsed on</li> <li>VERSION = the version of the built database</li> </ul> For all practical purposes the TAXONOMY should coincide with the taxonomic classification of your amplicons or genomes. For instance, if you used kraken2 and bracken2 you should use a model database with TAXONOMY = ncbi. Note that some taxonomy databases (GTDB, GreenGenes) prefix taxonomy identifiers with a rank indentifier like `s__Species`. Those are usually maintained in the databases here except for the NCBI Taxonomy which usually does not use those. You can verify taxon names using the manifests in the `release` section of http://github.com/micom-dev/databases . <br><br> For growth media that can be used with the model databases here please see the MICOM media repository at https://github.com/micom-dev/media.
Simple Biosphere model version 4.2 (SiB4) simulations for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb OCS.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figures 1 and 2.</p> <p>Simple Biosphere model version 4.2 (SiB4, Haynes et al., 2019; Sellers et al., 1986) was used to calculate (i) the average increase in evapotranspiration anticipated under an elevated OCS scenario for the years 2000-2021 on a 0.5 ° latitude x 0.5 ° longitude grid and (ii) OCS uptake by plants and soils, per month, at baseline (500 ppt) and elevated (4.8 and 35.5 ppb) OCS levels averaged over the years 2000-2021.</p> <p>- - - - - - - - - - -</p> <p><em>File 1: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_DeltaEvapotranspiration_GloballyGridded_SiB4.nc</em></p> <p>File Format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> percent_diff_et: relative increase in % of evapotranspiration in a scenario where 20% of terrestrial plants exhibit a 50% increase in stomatal conductance under high OCS</p> <p>- - - - - -</p> <p><em>File 2: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_BiosphereUptake_MonthlyIntegrated_SiB4.csv</em></p> <p>File Format:</p> <p> comma delimited text file (.csv)</p> <p>Index Variable:</p> <p> time: monthly, format m/dd/yy</p> <p>Parameters:</p> <p> ocs_veg_base: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_soil_base: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_veg_4.8ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_soil_4.8ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_veg_35.5ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 35.5 ppb</p> <p> ocs_soil_35.5ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., and Haynes, J. M.: Representing<br> Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple<br> Biosphere Model (SiB4), Journal of Advances in Modeling Earth Systems, 11, 4423-4439, 10.1029/2018ms001540, 2019.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Sellers, P. J., Mintz, Y., Sud, Y. C., and Salcher, A.: A Simple Biosphere Model (SiB) for Use within General Circulation Models, Journal of the Atmospheric Sciences, 43, 505-531, 1986.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) ,</p>
Files associated with Christopher Holder and Anand Gnanadesikan, How well do Earth System Models capture apparent relationships between phytoplankton biomass and environmental variables? [Version 1]
<p><strong>1. process_cmip_rf.m</strong> is a matlab script that reads a single file, generates a random forest using the parameters in the associated paper and computes permutation importance and sensitivities. Note- in order to get process_cmip_rf.m to work as written you must have the Statistics and Machine Learning toolbox installed on Matlab and download the table_modis.asc file below. </p> <p>Files 2-16 are tabular filew containing all datapoints used in Random Forest analysis for the NCAR CESM2 model. Columns are</p> <p> 1. Index of point, enabling a mapping back to the model grid if the resolution is known.</p> <p> 2. Longitude</p> <p> 3. Latitude</p> <p> 4. Month</p> <p> 5. Iron in mol/m<sup>3</sup>.</p> <p> 6. Mixed layer in m.</p> <p> 7. Ammonia in mol/m<sup>3</sup></p> <p> 8. Nitrate in mol/m<sup>3</sup>.</p> <p> 9. Phytoplankton carbon in mol/m<sup>3</sup>.</p> <p> 10. Phosphate in mol/m<sup>3</sup>.</p> <p> 11. Shortwave radiation (net solar radiation at ocean surface in W/m<sup>2</sup>).</p> <p> 12. Silicate in mol/m<sup>3</sup>.</p> <p> 13. Salinity in PSU</p> <p> 14. Temperature in C.</p> <p> 15. Upwelling velocity in m/s.</p> <p>If variable is not included in the dataset, the column will be filled with zeros.</p> <p><strong>2.table_cesm2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM model output prepared for CMIP6 CMIP esm-pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.7579">http://doi.org/10.22033/ESGF/CMIP6.7579</a>. Grid is 360x180x12</p> <p><strong>3.table_cems2_fv2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM-FV2 model output prepared for CMIP6 CMIP pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.11301">http://doi.org/10.22033/ESGF/CMIP6.11301</a>. Grid is 360x180x12</p> <p><strong>4. table_cesm2_waccm.asc: </strong>Data created from Danabasoglu, G., 2019, NCAR CESM2-WACCM model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.10094">http://doi.org/10.22033/ESGF/CMIP6.10094</a>. Grid is 360x180x12</p> <p><strong>5. table_cesm2_waccm_fv2.asc</strong>: Data created from Danabasoglu, G., 2019, NCAR CESM-WACCM-FV2 model output prepared for CMIP CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.11302">http://doi.org/10.22033/ESGF/CMIP6.11302</a>. Grid is 360x180x12</p> <p><strong>6. table_gfdl_cm4.asc</strong>: Data created from Guo, Huan; John, Jasmin G; Blanton, Chris et al,2018, NOAA-GFDL GFDL-CM4 model output piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8666">http://doi.org/10.22033/ESGF/CMIP6.8666</a>. Grid is 360x180x12</p> <p><strong>7.table_gfdl_esm4.asc</strong> Data created from Krasting, John P.; John, Jasmin G; Blanton, Chris et al., 2018, NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8669">http://doi.org/10.22033/ESGF/CMIP6.8669</a>. Grid 360x180x12</p> <p><strong>8. table_ipsl_cm5a2_inca.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al.: 2021, IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.13683">http://doi.org/10.22033/ESGF/CMIP6.13683</a>. Grid is 182x149x12</p> <p><strong>9.</strong> <strong>table_ipsl_cm6a_lr.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al., 2018:, IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5251">http://doi.org/10.22033/ESGF/CMIP6.5251</a>. Grid is 362x332x12.</p> <p><strong>10</strong>. <strong>table_mpi_esm1-2-ham.asc:</strong> Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe et al., 2019: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5037">http://doi.org/10.22033/ESGF/CMIP6.5037</a>. Grid is 256x220x12.</p> <p><strong>11</strong>. <strong>table_mpi_esm1-2-hr.asc:</strong> Data created from Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann et al., 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.6674">http://doi.org/10.22033/ESGF/CMIP6.6674</a>. Grid is 802x404x12.</p> <p><strong>12</strong>. <strong>table_mpi_esm1-2-lr.asc:</strong> Data created from Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann et al. 2019:MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl</p> <p> <a href="http://doi.org/10.22033/ESGF/CMIP6.6675">http://doi.org/10.22033/ESGF/CMIP6.6675</a>. Grid is 256x220x12.</p> <p><strong>13. </strong><strong>table_noresm2-lm.asc: </strong>Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo et al.,2019 NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8217">http://doi.org/10.22033/ESGF/CMIP6.8217</a>. Grid is 360x385x12</p> <p><strong>14.</strong><strong> table_noresm2-mm.asc</strong>: Data created from Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind et al.,2019 <strong>:</strong> NCC NorESM2-MM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8221">http://doi.org/10.22033/ESGF/CMIP6.8221</a>. Grid is 360x385x12.</p> <p>15-16. <strong>table_kostadinov.asc, </strong><strong>table_modis.asc</strong> Data is a merger of observational products and model output Observational climatologies for temperature, salinity, mixed layer depth, silicate, phosphate, and nitrate were downloaded from the World Ocean Atlas (WOA) 2018 (Garcia et al., 2019; Locarnini et al., 2019; Zweng et al., 2019). MODIS-POC was downloaded from oceancolor.nasa.gov. Kostadinov POC is taken from <a href="https://doi.pangaea.de/10.1594/PANGAEA.859005">https://doi.org/10.1594/PANGAEA.859005</a> Grid is 360x180x12.</p>
FiTraM: Field Traffic Model - First Version
<p>This is the first version of the python application FiTraM.</p> <p>FiTraM is the name for 'Field Traffic Model'. It models and reconstructs the tire tracks, wheel passes, wheel load and soil stress of agricultural machinery on the field based on route points either from GPS tracking, route planning or manual route construction.</p> <p>The model is described in Detail in:<br> Augustin, K., Kuhwald, M., Brunotte, J., Duttmann, R. (2019): FiTraM: A model for automated spatial analyses of wheel load, soil stress and wheel pass frequency at field scale. In: Biosystems Engineering 180, S. 108–120.</p> <p>This is part of the Ph.D. Thesis:<br> "Spatial Modeling of Field Traffic Intensity" from Katja Augustin, Kiel University</p>
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model - model outputs
<p>This dataset contains the model outputs used in the figures in the paper entitled "Implementing the iCORAL (version 1.0) coral reef CaCO<sub>3</sub> production module in the iLOVECLIM climate model" submitted to GMD. For the description of the model and simulations we refer to this article.</p> <p>Provided files:</p> <ul> <li>Surface values of temperature (temp), salinity (salt), phosphate (opo4) and aragonite saturation state (omega) for:</li> </ul> <p>The modern period (mean of 2000-2010): <strong>temp_modern.nc</strong>, <strong>salt_modern.nc</strong>, <strong>opo4_modern.nc</strong>, <strong>omega_modern.nc</strong></p> <p>The pre-industrial (PI, mean of last 100 years of the simulation): <strong>temp_PI.nc</strong>, <strong>salt_PI.nc</strong>, <strong>opo4_PI.nc</strong>, <strong>omega_PI.nc</strong></p> <ul> <li>Coral location for:</li> </ul> <p>Imin=50 μE/m2/s: <strong>coral_location_Imin50.nc</strong></p> <p>Imin=300 μE/m2/s: <strong>coral_location_Imin300.nc</strong></p> <p>The values indicate:</p> <p>4 = presence of corals in the model simulation (coral area less or equal to 5% of the grid cell area) but not in observations</p> <p>3 = presence of corals in both model and observational data</p> <p>2 = presence of corals in observational data but not in the model simulation</p> <p>1 = presence of corals in the model simulation (coral area more than 5% of the grid cell area) but not in observations</p> <ul> <li>Global coral reef area (10<sup>3</sup> km<sup>2</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_area_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>k</sub></em> (the saturating light intensity, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Ik.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>g<sub>max</sub></em> (the maximum production growth): <strong>Total_prod_vs_gmax.txt</strong></li> <li>Global carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and global coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_prod_vs_total_area.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and global production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>): <strong>Total_production_vs_rmse_Perry.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_area_vs_rmse_Perry.txt</strong></li> </ul>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
Open the record for dataset details and reuse information.
Community Land Model version 4.5 (CLM4.5) simulations of water, energy, and carbon fluxes for Saddle vegetation communities, 2008 - 2013
Single point simulations of CLM4.5 that include (1) forcing data that were input to the model and subsequent (2) model output for simulations that approximate conditions in fellfield, dry meadow, moist meadow, wet meadow, and snowbed vegetation communities. Forcing data were generated with observed atmospheric conditions from Tvan, Saddle precipitation, and incoming shortwave radiation measured from the AmeriFlux tower site (US-NR1) from 2008-2013. Wintertime precipitation inputs were modified to approximate average snow depth for each vegetation community observed across the Saddle grid. Land models, like CLM, provide a cohesive framework to investigate biogeophysical and biogeochemical effects of environmental change on ecosystem processes. We used CLM4.5 to investigate if a global-scale model can represent local-scale patterns of water, energy, and carbon fluxes in a heterogeneous mountain environment. Specifically, we were interested in generating testable projections of potential ecosystem responses to climate change. Model output includes half-hourly data on fluxes of energy, water, and carbon, as well as vegetation carbon stocks and edaphic conditions. We also conducted sensitivity analyses to look at ecosystem responses to modifications intended to extend growing season length by decreasing snow albedo and warming air temperatures (black sand and M-A warm, respectively). Information on the variables, units, and data are included as attributed in the network Common Data Form (NetCDF) files for this dataset. For users unfamiliar with using NetCDF files, we have included R scripts that write (forcing data) and read (model output) .nc files include in this data archive. More information about NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/docs/index.html.
3DAnatomicalRatModel: A printable version of a 3D anatomical rat model
<p>This is the version 1.0 of our static, 3D printable, anatomical rat model. The designed purpos is to provide a anatomical shaped phantom for medical imaging. The organs are constructed as hollow bodys, which can be filled with contrast agend suiteable for the technology of interest, e.g. iodine for CT, gadolinium for MRI or iron nanoparticles for MPI. Thus, it can be used as reference data or for experiment planing.</p> <p>In addition, it can be used for teaching purposes, for people making first steps in preclinical research.</p> <p>For best results, it is recommended to use the .form files to print the model in the optimized orientation on a Form2 or Form3 printer. On other printers the .stl files should be used.</p> <p>A publication using this phantom can be found here: <a href="https://doi.org/10.1515/cdbme-2019-0048">https://doi.org/10.1515/cdbme-2019-0048</a></p>
Ecore version of the meta-model for information dashboards (v2)
<p>The dashboard metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. This version includes the addition of a task taxonomy and a goal taxonomy for information visualization.</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>
0-D BiOeconomic mArine Trophic Size-spectrum (BOATS) model (review version)
<p>This is version 1.0 of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model, a bioenergetically-constrained coupled fisheries-economics model for global studies of harvesting and climate change. The code is written in MATLAB version R2012a. This release contains the zero-dimensional version of the code that models a single oceanic grid cell. This release is part of the peer review for the journal Geoscientific Model Development, which was published as Carozza et al. (2016) [Carozza, D. A., Bianchi, D., and Galbraith, E. D.: The ecological module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry, Geosci. Model Dev., 9, 1545-1565, doi:10.5194/gmd-9-1545-2016, 2016.].</p>
Data for Final Revised Version of "Photoacclimation and Photoadaptation Sensitivity in a Global Ecosystem Model"
<p>This repository contains the data and codes used to create the figures for the REVISED VERSION of the manuscript "Photoacclimation and Photoadaptation Sensitivity in a Global Earth System Model", by Charles A. Stock, John P. Dunne, Jessica Y. Luo, Andrew C. Ross, Nicolas Van Oostende, Niki Zadeh, Theresa J. Cordero, Xiao Liu and Yi-Cheng Teng. It also contains the COBALT Fortran codes used to generate the simulations. </p> <p>This article has been accepted to the Journal of Advances in Modeling the Earth System (JAMES). This update includes minor corrections implemented during proof corrections to ensure that all Figures and Tables were consistent in their treatment of data from the high Arctic (> 85 deg. N Latitude). The updates can be seen in minor changes to the "residual_and_skill" files.</p> <p>The codebase used for this work can be found in the following Github repository:</p> <p>https://github.com/NOAA-CEFI-Regional-Ocean-Modeling/ocean_BGC/releases/tag/COBALTv3_202501</p>
NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: Jan 1980 - Jul 2024
<p><strong>Overview</strong></p> <p>The NASA GSFC-FDM v1.2.1 provides the evolution of firn air content (FAC), surface mass balance (SMB) (and its individual components), and total firn height change over the Greenland and Antarctic Ice Sheets from January 1, 1980 to July 30, 2024 at 5-day temporal resolution. The model uses atmospheric forcing from NASA GMAO's Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global atmospheric reanalysis, combined with a higher resolution replay (see Medley et al., 2022) as input into the Community Firn Model (CFMv1.1.6) to simulate the evolution of firn properties across the ice sheets. The GSFC-FDMv1.2.1 is provided on a 12.5 km x 12.5 km North/South Polar Stereographic Grid, depending on the ice sheet.</p> <p>For a thorough description of how the GSFC-FDMv1.2.1 was generated see Medley et al. (2022) in <em>The Cryosphere</em>. Release 2 contains model output up through June 30, 2022, whereas the initial release only extended through September 30, 2021. Release 3 contains model output up through July 31, 2024 and uses CFMv2.3.1. The model set up is identical between releases.</p> <h3>*** The spatial grids are incorrect in this version, so we have restricted access to these files. Please use Version 4. ***</h3>
Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures
<p>Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures</p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
ScienceDex guides
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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.