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252 results for “Variability modeling”
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
<p>This dataset contains simulated and observed maps of surface temperature change over the satellite-era and domain averaged trends of tropospheric warming. The data accompanies software that was used to disentangle the forced and unforced components of tropical tropospheric temperature change. This work was documented in:</p> <blockquote> <p>Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, B.D. Santer (2022): "Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming," Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.2209431.</p> </blockquote> <p>The software is available at: https://github.com/LLNL/MDAS</p>
Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island / Model dataset
<p>Water vapor mixing ratios, isotopic composition of water vapor and precipitations associated with the manuscript:</p> <div> <div>Landais, A., Agosta, C., Vimeux, F., Magand, O., Solis, C., Cauquoin, A., Dutrievoz, N., Risi, C., Leroy-Dos Santos, C., Fourré, E., Cattani, O., Jossoud, O., Minster, B., Prié, F., Casado, M., Dommergue, A., Bertrand, Y., and Werner, M.: Abrupt excursions in water vapor isotopic variability at the Pointe Benedicte observatory on Amsterdam Island, Atmos. Chem. Phys., 24, 4611–4634, https://doi.org/10.5194/acp-24-4611-2024, 2024.</div> </div>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
<p><em>Aim:</em> Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.</p> <p><em>Location: </em>Seamounts around the Cabo Verde Archipelago (NW Africa).</p> <p><em>Methods:</em> We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.</p> <p><em>Results</em>: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.</p> <p><em>Main conclusions:</em> Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.</p>
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable
<p>Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.</p>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
Figure 2 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 2. - Relation between cumulative trophic diversity and number of analyzed stomachs of Gaidropsarus guttatus from Faial Island, Azores.
Figure 3 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 3. - Index of relative importance (%Rw) regarding the major prey items found in the stomachs of Gaidropsarus guttatus from Faial Island, Azores.
Figure 1 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 1. - Map showing Faial, within the Azores Archipelago, NE Atlantic, and collection Gaidropsarus guttatus sites.
Time Variable Ionospheric Electric Field Model (TiVIE) light data v 1.0
<p>This is the TiVIE light data to accompany TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model
<p>This data is only used for paper submitted to the JPO entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.</p>
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
CCSM4 LR and HR Model Simulations for GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall"
<p>This archive contains model simulations of precipitation used in the GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall" (Zhang et al., 2021). These model simulations are standard control simulations based on the Community Climate System Model Version 4.0 (CCSM4) using eddying (HR) and eddy-parameterizing (LR) ocean component models. </p> <p><em><strong>In order to properly acknowledge those who have worked hard to create those data sets we do require that if this data is used in a publication that coauthorship be offered to those involved in the data creation. Please contact the author Dr. Wei Zhang (email: wz19@princeton.edu) for any further questions or potential collaboration. </strong></em></p>
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
Results from the FLEX Model for the paper "Impact of variable electricity price on heat pump operated buildings"
<p>The sqlite database contains the results of the Flex model for the Austrian single family house building stock ( insert GITHUB LINK). The building stock is represented by 36 different representative building archetypes (“OperationScenario_Component_Building”). Each building is simulated in twice. In the "reference" mode the energy demand is simply met and indoor comfort is kept constant. In the "optimization" mode the indoor temperature can be varied and thermal storages are charged and discharged minimizing the households energy cost based on a variable electricity price. The sqlite database “Variable_Price_Paper” contains the results for the building stock without any storage implemented. In “Variable_Price_Paper_TS” all buildings have a 750l hot water buffer storage and a 400l DHW storage implemented. The sqlite files SFH_23/25/27 contain the results for a single building where the maximum inside room temperature was changed to 23, 25 and 27 °C respectively.</p> <p>Following columns were used for generating the results for the Paper:</p> <p>In the hourly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario: each building simulated under a different electricity price has a unique scenario number. “OperationScenario” gives an overview of the scenarios.</li> <li>Grid: describes the electricity demand from the grid by the household.</li> </ul> <p>In the yearly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario</li> <li>TotalCost: represent the yearly operation cost</li> <li>Grid: electricity demand summed up for the whole year</li> </ul> <p>5 different electricity prices are used for scenario generation. The first price is constant, “electricity_2” is the real time price from 2021 plus a hypothetical grid fee of 20cents/kWh. “electricity_3, electricity_4, electricity_5” are prices generated for 2030 for Austria with the Balmorel model. Their profiles can be found under “OperationScenario_EnergyPrice”.</p> <p> </p>
Reuse of Model Transformations for Propagating Variability Annotations in Annotative Software Product Lines - Evaluation Data
<p>This package contains all data that was produced for and used in the doctoral thesis for evaluating commutativity of propagating annotations in model-driven product lines.<br> This includes the implementation that conducts the evaluation, the measured results, and the input subjects.</p>
Data from: Hidden variable models reveal the effects of infection from changes in host survival
<p class="MsoNormal">The impacts of disease on host vital rates can be demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual effects of infectious disease from population-level measurements of survival when longitudinal studies are not possible. Our approach <span>seeks to explain temporal deviations in population-level survival after introducing a disease causative agent when disease prevalence cannot be directly measured by coupling survival and epidemiological models. We tested this approach using an experimental host system (<em>Drosophila melanogaster</em>) with multiple distinct pathogens to validate the ability of the hidden variable model to infer per-capita disease rates. We then applied the approach to a disease outbreak in harbor seals (<em>Phoca vituline</em>) that had data on observed strandings but no epidemiological data. We found that our hidden variable modeling approach could successfully detect the per-capita effects of disease from monitored survival rates in both the experimental and wild populations. Our approach may prove useful for detecting epidemics from public health data in regions where standard surveillance techniques are not available and in the study of epidemics in wildlife populations, where longitudinal studies can be especially difficult to implement.</span></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>
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.