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2,152 results for “outputs”
FOCI model output used in the study by Ivanciu et al. - Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean
<p>This dataset comprises the output from simulations with the coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020) used in the analysis presented in the study by Ivanciu et al., 2021 “Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean”. Four ensembles of three simulations each were performed: FixODS (II012, II014, II016), FixGHG (II013, II015, II017), INTERACT_O3 (SW128, II010, II011) and PRESC_O3 (JH027, II037, JH039). A detailed description of the simulations can be found in the above-mentioned manuscript.</p>
Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
<p>Epidemics can sometimes be managed through reductions of host density, such as social distancing for human diseases, reducing plant density through cultural and genetic means, and host culling for epizootics. These approaches allow for a certain density of hosts to remain within a targeted area. By contrast, total ring depopulation is often used as a management strategy for emerging infectious diseases in livestock. In this study, we explore the trade-offs of a density-based culling strategy to determine if fewer livestock farms can be culled within rings while maintaining a decrease in disease transmission. To do so, we evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom. This strategy may allow for some farms within rings around infected premises (IPs) to escape depopulation, with the aim to prevent over-culling during outbreaks. Using a spatially-explicit, stochastic, state-transition simulation algorithm originally developed by Keeling et al. 2001 to model FMD spread in the United Kingdom, we simulated this reduced-farm-density, or "target density" strategy. We modeled FMD disease spread in four counties in the UK (Aberdeenshire, Cumbria, Devon, and North Yorkshire) that have different farm demographies. We ran 740,000 simulations in a full-factorial analysis of epidemic impact measurements (i.e. culled animals, culled farms, epidemic length) and cull strategy parameters (i.e. target farm density, daily farm cull capacity, cull radius). We found that all of the cull strategy parameters were drivers of epidemic impact. We found that outbreaks in Cumbria had higher epidemic impacts and were more likely to take off compared with other counties with more outbreaks being likely to take off in Cumbria. Most importantly, in all counties, our proposed target density strategy was more effective at combatting FMD compared with traditional 'total ring depopulation' when considering average culled animals and culled farms. The differences in epidemic impact between the counties are likely driven by farm demography, especially differences in cattle and farm density. This target density strategy can be applied to many different systems, including other livestock and agricultural systems, to reduce host density as opposed to over-culling hosts.</p>
Digitized Raw Output Signals of a Low-cost Aerosol PM Sensor Sharp GP2Y
<p>The data contains the digitized traces of low-cost PM sensor Sharp GP2Y1010AU0F pulse outputs during calibration and environmental measurements. The work was done within the Aeromet EMPIR project 19ENV08.</p> <p>Further details are given in the article:</p> <p>Bučar, K.; Malet, J.; Stabile, L.; Pražnikar, J.; Seeger, S.; Žitnik, M. Statistics of a Sharp GP2Y Low-Cost Aerosol PM Sensor Output Signals. Sensors 2020, 20, 6707. <a href="https://doi.org/10.3390/s20236707">https://doi.org/10.3390/s20236707</a></p> <p>Also see the included README.pdf file.</p> <p> </p>
Model output files for Ou et al. 2021 (Deep Mitigation of CO2 and non-CO2 Greenhouse Gases towards 1.5°C and 2°C Futures)
<p>GCAM model output files used to reproduce Ou et al. (Deep Mitigation of CO<sub>2</sub> and non-CO<sub>2</sub> Greenhouse Gases towards 1.5°C and 2°C Futures)</p>
NOAA-GFDL GFDL-ESM4 CMIP6 CMIP additional model output
<p>This repository contains annual 3D sea water age since surface contact files from a subset of simulations with GFDL's ESM4.1 climate model (Dunne et al., 2020) that were contributed to the 6th Coupled Model Intercomparison Project (CMIP6). </p> <p>Both the ocean model native grid and the regridded output can be found in this repository. For the regridded output, the files have been regridded from MOM6 native grid to standard World Ocean Atlas 1x1 horizontal grid, and they have been remapped from the lagrangian vertical coordinate used by GFDL's MOM6 ocean model (Adcroft et al., 2019) to standard World Ocean Atlas depths.</p> <p>Files have undergone QA/QC.</p> <p>All data files are NetCDF.</p> <p>Enquiries should be directed to Jasmin.John (Jasmin.John@noaa.gov) or John Dunne (John.Dunne@noaa.gov)</p> <p>Anyone using these data should cite Dunne et al. (2020) and Adcroft et al. (2019). See references below.</p> <p>Other variables associated with these simulations, along with assigned DOI's, are available through the ESGF CMIP6 portal:https://esgf-node.llnl.gov/projects/cmip6/ </p> <p>______________________________________________________________________</p> <p>The tar files in the repository follow this naming convention: <model>_<activity>_<MIP>_<experiment>_<component_and_grid>_<variable>.tar.gz</p> <p> </p> <p>E.g. </p> <p> </p> <p>GFDL_ESM4_CMIP6_CMIP_historical_ocean_annual_z_1x1deg_agessc.tar.gz (regridded)</p> <p>GFDL_ESM4_CMIP6_CMIP_historical_ocean_annual_z_agessc.tar.gz (native grid)</p> <p> </p> <p>The organization of above repository is as follows:</p> <p>Experiments:</p> <p>The concentration-forced piControl simulation: </p> <p>NOAA-GFDL/CMIP6/CMIP/GFDL-ESM4/ESM4_piControl_D</p> <p>The concentration-forced historical simulation: </p> <p>NOAA-GFDL/CMIP6/CMIP/GFDL-ESM4/ESM4_historical_D1</p> <p>The historical simulation was spawned from year 101 of the pre-industrial control simulation</p> <p>For each experiment, 3D annual sea water age data can be found in the sub-directory:</p> <p>/ocean_annual_z_1x1deg (for regridded output)</p> <p>/ocean_annual_z (for native grid output)</p> <p>_________________________________________________________________________</p> <p>NOTES:</p> <p>Provenance:</p> <p>The data provided here do not have metadata that can be used for provenance </p> <p>and traceability back to NOAA/GFDL.</p> <p>The data DOI assigned should be used when sharing and citing these data.</p> <p>_________________________________________________________________________</p> <p><br> </p>
Outputs from Isca perturbed parameter ensemble (PPE) simulations (Part I)
<p>Outputs from Isca perturbed paramter ensemble (PPE) simulations under 1xCO2 (control run) and 4xCO2 (perturbed run), in which the simulations are prescribed with Q-flux.</p> <ul> <li>cld_fbk_cmp.zip: The outputs used for the comparison of cloud feedback computation methods.</li> <li>qflux_clisccp_data.zip: The outputs contain the variable clisccp from control and perturbed runs, used for the cloud feedabck calculation</li> <li>qflux_extracted_data_toa_flux_30yr.zip: The last 30-year top of the atmosphere (TOA) flux data used for deriving the equilibrium climate sensitivity, total climate feedback and effective radiative forcing through the Gregory plot</li> <li>qflux_extracted_data.zip: The last 5-year simulation outputs for control and perturbed runs.</li> <li>qflux_extracted_data_10yr.zip: The last 10-year simulation outputs for control and perturbed runs.</li> </ul> <p>This is Part I, and Part II can be found at: <a href="https://doi.org/10.5281/zenodo.5188175">10.5281/zenodo.5188175</a></p>
MITgcm model setup and output for "Impact of subglacial freshwater discharge on Pine Island Ice Shelf"
<p>Here, it contains the results of the regional PIG simulation. Model grid is lat-lon similar to Nakayama et al., 2019 roughly 200 m in the region. See Nakayama et al., submitted to GRL for detail. </p> <p>For complete model output (10*Qsg and 50*Qsg cases), please access NASA data (Registration is required).<br> https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/PIG_only_200m </p> <p>Contents can be downloaded easily using wget (see link below). <br> https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> latlon_run23.zip (CTRL)<br> latlon_run24.zip (Qsg case)<br> latlon_run25.zip (2*Qsg case)<br> <br> <strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx_it50/ .<br> cp ../build/mitgcm_uv .<br> qsub latlon_run23.sh</p>
MAgPIE v4.3.x model run outputs including dynamic forestry sector
<p>Archive of runs produced for the forestry paper using MAgPIE 4.3.1+</p> <p><a href="https://github.com/magpiemodel/magpie">Model Code</a></p> <p><a href="https://rse.pik-potsdam.de/doc/magpie/4.3/index.htm">Model documentation</a></p> <p><a href="https://github.com/magpiemodel/tutorials">Model tutorials</a></p>
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21<sup>st</sup> century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards.</p> <p>In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes.</p> <p>Our abstract reads:</p> <p>“The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.</p> <p> </p> <p><strong>Citation.</strong></p> <p>Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script.</p> <p>Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea. Data, scripts and model output
This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019).
Model setup and output for 'Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude'
<p><strong>Data supplement to Hughes and Klymak 2019</strong></p> <p>Model input and output in a reduced form associated with the following paper:</p> <p><strong>Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude<br></strong><em>Journal of Physical Oceanography.</em> <a href="http://dx.doi.org/10.1175/JPO-D-18-0132.1">doi:10.1175/JPO-D-18-0132.1</a></p> <p><strong>Inputs</strong></p> <p>As described in Table 1 of the associated paper, there are three main sets of simulations. The input files for these sets are contained in their respective directories (`vary_width`, `vary_forcing`, and `vary_freq`). The python script that creates all of the necessary files is `gendata.py`. A fourth directory is titled `baroclinic_terms` and includes that simulation in which <em>u'</em> and <em>p'</em> are output at high temporal resolution.</p> <p>A key point regarding the input files is that for the vary width and vary forcing cases, a single simulation involves multiple channels. This lets me compile a single executable `mitgcmuv` with a Nx × Ny grid of 600 × 1280, which I divide up into the necessary number of channels by putting vertical walls in appropriate places. For the vary width cases, the 'narrow' simulations are all channels from 0.2 to 32 km and the 'wide' simulations are all wider channels. Once the simulation has run, I use netcdf tools (`ncks`) to extract the individual channels using the scripts in the `extract_scripts` directory.</p> <p>Most of the files in the `code` directories will be familiar to anyone that uses the MITgcm. An exception is the `energy_diagnostics_fill.F` (and `diagnostics_main_init.F` and `do_statevars_diags.F`, which have minor additions). The original, from `https://github.com/jklymak/MITgcmcode`, was modified slightly to suit this project.</p> <p><strong>Outputs</strong></p> <p>The results directory contains five subdirectories to be described in turn.</p> <p>Notes that in all cases, energy terms in the netCDF files do not include a factor of ρ. This was added in at the plotting stage.</p> <p>All simulations used Checkpoint67b and were run on Graham: https://docs.computecanada.ca/wiki/Graham.</p> <p><strong>vary_width</strong></p> <p>The majority of the files are of the form `obstacle_FFF_YY.nc` where `FFF` is $1000 ω/f$ and `YY` is the channel width in kilometres. These files contain the tidally averaged, depth-integrated energy diagnostics for the seventh tidal cycle at all points (<em>x, y</em>) within the energy control volume.</p> <p>There are also three files entitled `tophat_995_YY.nc`, which contain fields of <em>U</em>, <em>V</em>, and <em>T</em> (which gives density with α = 0.0002) at two levels. These fields are used as examples for weakly and strongly responding channels.</p> <p><br><strong>vary_forcing</strong></p> <p>These files are of the form `forcing_UU.nc` where `UU` is the deep-water tidal current amplitude <em>U_</em>0 in cm/s. They contain the same energy terms as for the vary width simulations.</p> <p><strong>vary_freq</strong></p> <p>These files are of the form `freq_FFF_fields.nc` and contain fields of <em>U</em>, <em>V</em>, and <em>T</em> at two levels. Energy terms are not included because the vary frequency simulations were only run to get estimates of the along-ridge wavelength.</p> <p><strong>baroclinic_terms</strong></p> <p>The single file within this directory contains <em>u'</em> and <em>p'</em> at a single <em>x</em> position every five minutes for four tidal cycles.</p> <p><strong>gaussian_26</strong></p> <p>This directory, named for its obstacle and width, contains <em>U</em>, <em>V</em>, and <em>T</em> at every grid point for a snapshot in time and another file with the corresponding snapshots of all energy terms.</p>
Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Output files of the RQSS extractor and framework on 3 Topical (Gene Wiki, Music, Ships) subsets and 4 Random Subsets
<p>This dataset contains the `.csv` output files of the Referencing Quality Scoring System - RQSS performed on 3 topical subsets (Gene Wiki, Music, Ships) and 4 random subsets. Subset RDF files are in <a href="https://doi.org/10.5281/zenodo.7332161">this dataset</a>.</p>
Variable-resolution CESM2 over Antarctica (ANTSI): Monthly outputs used for evaluation.
<p>Funding generously provided by NSF Award 1952199 as well as additional support from NCAR in the creation of the dataset and the use of VRCESM. </p> <p>Included are outputs from an AMIP-style (data-driven ocean and sea ice) simulation of Variable-Resolution CESM2 from 1979-2015. Resolution is 1° globally with a refined 0.25° resolution over the Southern Ocean as well over the Antarctic ice sheet. Outputs are at a monthly timescale, and include those variables relevant for evaluation.</p> <p>Each netcdf file ends with several relevant tags to indicate <source>.<output deisgnator>.<variable>.<time period>.nc. Atmospheric variables are labeled with "cam" whereas ice sheet variables are labeled with "clm2".</p> <p>clm2.h0.SNOW.198901-199812.nc</p> <p>cam.h1.Q.198901-199812.nc</p> <p>Variables are described for CESM2 (see NCAR documentation for clm and cam)</p> <p>Variables included for cam include FLDS, FLNS, CLDICE, CLDLIQ, LHFLX, PRECC, PRECL, PRECSC, PS,Q, SHFLX, U, V, Z3</p> <p>Variables included for clm2 include FIRA, FIRE, FLDS, FSDS, FSH, QICE, QRUNOFF, QSNOMELT, QSOIL, RAIN, SNOW</p> <p> </p>
Output of the Global Crop Water Model GCWM
<p>Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes.</p> <p>Format: ASCII-grids (global, 5 arc-minute) or text files (country data).</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
TROVA outputs for the extratropical transition of tropical cyclones in the North Atlantic basin
<p>Output data from the TRansport Of water Vapor (TROVA) tool for the research paper "Evaluating changes in the moisture sources for tropical cyclones precipitation in the North Atlantic that underwent extratropical transition".</p>
Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data
<p>This dataset contains model output for the simulations presented in: <em>"Multi-proxy agreement on Atlantic circulation dynamics since the last ice age"</em>.</p> <p> </p>
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
<p>We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.</p>
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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.