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695 results for “model output”
Key input and output data for the multi-model analysis "Open Source Energiewende"
<p>This repository contains key input and output data of the multi-model analysis carried out in the project "Open Source Energiewende", financed by the German Federal Ministry for Economic Affairs and Energy.</p> <p>The results are presented and discussed in the paper "Power sector effects of cheaper stationary batteries: insights from an open multi-model analysis".</p> <p>The model codes are availabe in individual repositories, which are provided in the paper.</p>
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Model output data for Marine Wild-Capture Fisheries after Nuclear War
<p>This is the model data material for Scherrer at al., PNAS. [Scherrer K. J. N., et al. Marine wild-capture fisheries after nuclear war, PNAS in press]. Input files include time series of gridded global oceanic Sea Surface Temperature and Net Primary Productivity (output from the CESM model) and socioeconomic input for the global fisheries model (BOATS). Output files include globally integrated time series (used in Figs. 1-3 and 6) and global gridded model output (used in Figs. 4-5) for each of the five ensemble member runs.</p>
A quantitative method to calibrate the SWAN wave model - pre-processed model outputs
<p>The pre-processed MAT files for the paper 'A quantitative method to calibrate the SWAN wave model'</p>
Model output associated with Bucciarelli et al. "Western Indian subantarctic phytoplankton blooms fertilized by iron-enriched Agulhas water"
<p> Annual mean sea surface elevation (zeta), surface chlorophyll (chl), surface DFE (fe0), 500 m DFE (fe500), surface salinity (salt0) and 500 m salinity (salt500) from CROCO PISCES ocean model for two simulations (reference ASAP2 and NAM without Fe sources along African margins).This is associated with publication: Bucciarelli et al. "Iron-enriched Agulhas waters fertilise the Indian Subantarctic Zone"</p>
Project Pythia: Output from WRF V3.6 model
Project Pythia is a community resource for learning how to analyze geosciences data using the Scientific Python Ecosystem. This data set is used primarily on the WRF-Python webpage to illustrate how WRF-Python can be used in conjunction with the Python packages Cartopy, Matplotlib, and Basemap. The data set is also used in a vertical interpolation example that is also found on the WRF-Python webpage. The central latitude for the data is 38.5N and the central longitude is 97.5W.
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></p>
Model outputs for "Modeling the day-night temperature variations of ultra-hot Jupiters: confronting non-grey general circulation models and observations"
<p>SPARC/MITgcm general circulation model outputs, including temperature structure, atomic hydrogen mixing ratio, winds, and time-resolved spectra produced by PICASO, for the paper "<strong>Modeling the day-night temperature variations of ultra-hot Jupiters: confronting non-grey general circulation models and observations" </strong>by Tan et al. (2023). </p><p>Plotting scripts in Matlab for almost all figures in the paper are provided. These scripts can be modified to read out and plot any outputs that are not shown in the paper. </p>
Colville River Delta Sea Ice Model and Output Data Files
<p>In this study, we used a 1D Delft3D-FLOW model to simulate the temporal development of the Colville River Delta, AK during the most active Arctic seasons. Simulations focused on the deltaic clinoform (i.e., the cross-sectional view of a delta) and used a floating barge structure to mimic the effects of sea ice on surface waters. Delft3D simulations were coupled with modules written in MATLAB and outputs were process in MATLAB.</p><p>The dataset includes an example model run file (Delft3D-FLOW and MATLAB) and output of results from simulations used to assess Arctic delta development under sea ice. </p><p>Files include:</p><ol><li>Example Delft3D-FLOW model setup file, MATLAB run script, and ice files for a 1500-year simulation.</li><li>Processed MATLAB structures and metadata for model results<ol><li>Long-term Delta Developmental Outputs (1500-year simulations)<ol><li>Ice-free</li><li>Ice-affected</li><li>Ice-free with waves</li><li>Ice-affected with waves</li></ol></li><li>Varying Sea Ice Characteristics Outputs (500-year simulations)<ol><li>Ice matrix (six simulations)</li></ol></li><li>Future Arctic Delta Scenarios Outputs (450-year simulations) <ol><li>Scenario A</li><li>Scenario B</li></ol></li></ol></li></ol>
Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management: Model output
<p>This archive includes the output from the General Ocean Turbulence Model for a series of simulations that used differing model drivers (inflow discharge, Q, and air temperature, T). The Experiment_output.zip contains text files generated from the GOTM workflow containing modelled water temperatures and the Mod_z.txt are the corresponding depths for these temperatures. </p><p>The file name corresponds to the change made to the driving data from baseline (unchanged conditions), a combination of air temperature <i>increase </i>and flow percentage change e.g. Mod_temp_T_2_Q_1.5 refers to an air temperature increase of 2 degrees Celsius and a flow increase of 50% and a Mod_temp_T_3.5_Q_0.7 refers to an air temperature increase of 3.5 degrees Celsius and a flow decrease of 30%.</p>
Model outputs and data used in the study
<p>The aqua_chl.rar is Chla satellite remote sensing data. </p><p>The Cempaka.txt is the Typhoon Cempaka data obtained fron CMA.</p><p>The Lupit.txt is the Typhoon Lupit data obtained fron CMA.</p><p>The PRE_mod.mat is the model outputs.</p><p>The diagnose_budget.rar is diagnostic output from the model.</p><p>The salinity.mat file contains both the measured and simulated salinity data.</p>
WRF model output
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Model Output for: "MESSENGER observations of Mercury's planetary ion escape rates and their dependence on true anomaly angle"
<p>In the manuscript titled ‘MESSENGER Observations of Mercury’s Planetary Ion Escape Rates and Their Dependence on True Anomaly Angle’, submitted to Geophysical Research Letters, we provide an analysis of test sodium ion (Na+) particles. These test particles, integral to our research, are illustrated in Figure 4. This dataset contains an ASCII file with detailed information and descriptions of these test particles.</p>
Model Output accompanying 'A predicted pause in the rapid warming of the Northwest Atlantic Shelf in the coming decade'
<p>Postprocessed model output and observations necessary for reproducing all figures from the main body of the artcile 'A predicted pause in the rapid warming of the1 Northwest Atlantic Shelf in the coming decade'. Please see the README.txt file </p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
Raw data and outputs from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
<p>The sensory trap model of signal evolution suggests that males manipulate females into mating using traits that mimic cues used in a nonsexual context. Despite much empirical support for sensory traps, little is known about how females evolve in response to these deceptive signals. Female sea lamprey (<em>Petromyzon marinus</em>) evolved to discriminate a male sex pheromone from the larval odor it mimics and orient only towards males during mate search. Larvae and males release the attractant 3-keto petromyzonol sulfate (3kPZS), but spawning females avoid larval odor using the pheromone antagonist, petromyzonol sulfate (PZS), which larvae but not males, release at higher rates than 3kPZS. We tested the hypothesis that migratory females also discriminate between larval odor and the male pheromone and orient only to larval odor during anadromous migration, when they navigate within spawning streams using larval odor before they begin mate search. In-stream behavioral assays revealed that, unlike spawning females, migratory females do not discriminate between mixtures of 3kPZS and PZS applied at ratios typical of larval versus male odorants. Our results indicate females discriminate between the sexual and nonsexual sources of 3kPZS during but not outside of mating and show sensory traps can lead to reliable sexual communication without females shifting their responses in the original context.</p>
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
Open the record for dataset details and reuse information.
Plastic damage in sea ice - model outputs and observations.
<p>Sea ice model outputs and observations analyzed in the paper: "On the sensitivity of sea ice deformation statistics to plastic damage".</p> <p>For the outputs, there is one file every 6 hours (for January 2002). Each folder represents one experiment with the associated snapshot files each containing a matrix of the given variable.</p> <p>The observations are the 3-day gridded velocity derivatives. The files are numpy arrays (.npy) containing time slices for every 3-day period for January 2002.</p> <p> </p> <p><strong>1. Naming convention of the files</strong></p> <p><em>Model outputs</em></p> <p>The folders are named according to the model configuration following the names used in (Savard and Tremblay, 2024):</p> <p>"Model name" + ( + "configuration" + )</p> <p>The files in each configuration follow the following convention:</p> <p>"Variable" + "year" + _ + "month" + _ + "day" + _ + "hour" + _ + "minutes" + . + "experiment number"</p> <p>There is also an info file, which provides the numerical parameters used in each run:</p> <p>info + . + "experiment number"</p> <p><em>Observations</em></p> <p>The files are the velocity derivatives:</p> <p>"partial derivative" + .npy</p> <p>There is also a mask for the period of January-February-March:</p> <p>mask80JFM.npy</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><em>dam</em>: plastic damage per grid cell (0 -- 1); </li> <li><em>div</em>: sea ice divergence per grid cell (in s^-1);</li> <li><em>shear</em>: sea ice shear per grid cell (in s^-1).</li> </ul> <p> </p> <p><strong>4. Recommended citation usage</strong></p> <p>If <em>any</em> or <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the plastic damage paper (Savard and Tremblay, 2024). If the observations provided here are used in a future study, we ask to cite this archive and the original paper in which the data is used (Kwok, 1997).</p> <p>The original RGPS 3-day gridded dataset can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/</p> <p> </p>
Modelling outputs of the AWESOME water supply model of the Nile River Basin
<p>This folder contains the output of the water supply model as trajectories for the simulated storage levels and water release for the Grand Ethiopian Renaissance Dam, Merowe Dam, and High Aswan Dam. The trajectories are simulated under both historical climatic and water demand scenarios, as well as for future climatic and demographic scenarios (RCP4.5 + SSP2).</p> <p>h_GERD_ : Grand Ethiopian Renaissance Dam water level</p> <p>h_MER_ : Merowe Dam water level</p> <p>h_HAD_ : High Aswan Dam water level</p> <p>r_GERD_ : Water release from Grand Ethiopian Renaissance Dam</p> <p>r_HAD_ : Water release from High Aswan Dam</p> <p>BestHP_hist: Best performance for hydropower production at the basin level under historical conditions of water availability and demand </p> <p>BestEgypt_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand </p> <p>BestSudan_hist: Best performance for irrigation deficit in Sudan under historical conditions of water availability and demand</p> <p>LowReduction_hist: Low water reduction solution under historical conditions of water availability and demand </p> <p>MediumReduction_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand </p> <p>LowReduction_2050: Low water reduction solution under climatic (RCP4.5) and demographic projection (SSP2) in 2050 </p> <p>MediumReduction_2050: Best performance for irrigation deficit in Egypt under climatic (RCP4.5) and demographic projection (SSP2) in 2050 </p>
Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs
<p>This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans. The outputs are presented in netCDF format and come from two simulations - <em><strong>GREEN</strong></em>, with chlorophyll, and <em><strong>BLUE</strong></em>, without chlorophyll affecting shortwave heating.</p> <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>BLUE</strong><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>GREEN</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>BLUE</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>BLUE </strong></em>experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>
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.