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2,152 results for “outputs”
Outputs of the Jupyter Notebook - Sea ice forecasting using IceNet
<p>The dataset contains the outputs of the notebook "Sea ice forecasting using IceNet" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> <li>Tom R. Andersson (reviewer), British Antarctic Survey, <a href="https://github.com/tom-andersson">@tom-andersson</a></li> <li>Nick Barlow (reviewer), The Alan Turing Institute, <a href="https://github.com/nbarlowATI">@nbarlowATI</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Tom R. Andersson (author), British Antarctic Survey, <a href="https://github.com/tom-andersson">@tom-andersson</a></li> <li>James Byrne (contributor), British Antarctic Survey, <a href="https://github.com/JimCircadian">@JimCircadian</a></li> <li>Tony Phillips (contributor), British Antarctic Survey</li> </ul>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Collective Variable for Metadynamics Derived from AlphaFold Output
<p>AlphaFold is the state of the art method for prediction of 3D structures of proteins from the amino acid sequence by neural networks. One of the outputs of AlphaFold is a probability profile of inter-residue distances for all residue pairs. We used this profile to evaluate any conformation of the studied protein to express its compliance with the AlphaFold prediction. This value can be used as a collective variable in metadynamics or parallel tempering metadynamics to accelerate protein folding in a molecular simulation. We applied this approach on folding of mini-proteins Trp-cage and beta hairpin. See V. Spiwok, M. Krečka & A. Křenek: <a href="http://doi.org/10.3389/fmolb.2022.878133">Collective Variable for Metadynamics Derived from AlphaFold Output</a> <em>Frontiers in Molecular Biosciences</em> <strong>9</strong> 878133 (2022) DOI: 10.3389/fmolb.2022.878133.</p>
Model output supporting MimiBRICK v1.1.0
<p>Model calibration output and model simulation output supporting the model calibration and analysis codes included in MimiBRICK v1.1.0.</p> <p>v1.1.0 edit: file naming simplified</p>
ZINC15 RECAP core fragmentation output
<p><strong>uwashed.zip</strong> --- washed molecules from ZINC15 mapping non-stereochemical SMILES to ZINC IDs.</p> <p><strong>frags.zip</strong> --- fragmented molecules</p> <p><strong>ViSAS - Example - Full.knwf </strong>---- a KNIME workflow with an exemplary implementation of ViSAS</p> <p>Consider that the folders were split for uploading. Before using any of our scripts, please join the corresponding folders after downloading. </p> <p>The fragmentation procedure followed conforms to the one published by Naveja et al., 2019: doi: 10.1186/s13321-019-0380-5</p> <p>Please cite the publication above should you use this data. Alternatively, we expect to publish in short a paper on this dataset.</p> <p> </p>
Microbes go to school - Output repository
<p>This dataset is an output repository for the final report of the Agora project "Microbes go to school" funded by SNF from 2020 to 2022. This project aims at using service-learning to bridge the gap between university and school, and disseminate knowledge in microbiology and biodiversity in the classroom by engaging students as communicators. In this repository, you'll find general content about the project (gallery, course descriptions, and the article we published), pedagogical content (protocols of the activities edited by us, original content produced by the students that was evaluated, and the feedback form that we sent to the teachers to evaluate the students), and outreach content (guide for trainers, newsletters and recipes).</p>
MIDAS2 protocol example output files
<p>Example output for MIDAS2 protocols including all the basic and supporting protocols.</p>
RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. </p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction. The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended. </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Data, scripts and model output to perform spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea
<p>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). </p>
Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
<p>The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
WACCM-X simulation output in support of publication "Impact of upward propagating migrating diurnal and semidiurnal tides on the ionosphere-thermosphere seasonal variation"
<p>This dataset contains simulation output from the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X) in support of the publication "Impact of upward propagating migrating diurnal and semidiurnal tides on the ionosphere-thermosphere seasonal variation". Data files include the simulation results for a five-member ensemble of free-running simulations, simulations without the upward propagating diurnal migrating tide (DW1), and simulations without the upward propagating semidiurnal migrating tide (SW2). </p>
West Nile Virus Predictions output data
<p><span>The testSubmission.csv was meant for validating the results in the Kaggle competition. It is a csv with 2 columns, one being a probability value from 0 to 1 and the second being an id to which that probability refers to. Those probabilities are the result of the classifications of a trained XGBoost classifier. </span></p>
Coverage and quality of open metadata for Dutch research output - dataset
<p>Record level data underlying the figures and tables in the report:<strong><br><br>Coverage and quality of open metadata for Dutch research output - report<br></strong></p> <p><a href="https://doi.org/10.5281/zenodo.10629457" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10629457</a><br><br>The current dataset contains 3 csv files:</p> <ul> <li><em>rpo_nl_list_long_20240201.csv</em> - list of identifiers (ROR ID, OpenAlex ID, OpenAIRE ID) of Dutch research performing organisations. <p>Identifiers were collected for the following groups of Dutch RPOs (see Appendix A):</p> <ul> <li> <p>Universities, organised in Universities of the Netherlands (UNL, n=14);</p> </li> <li> <p>University Medical Centres, organised in the Dutch Federation of University Medical Centres (NFU, n=9); </p> </li> <li> <p>National research institutes under the umbrella organisation of the Foundation for Dutch Scientific Research Institutes (NWO-i, n= 9);</p> </li> <li> <p>Research institutes of the Royal Netherlands Academy of Arts and Sciences (KNAW, n=10);</p> </li> <li> <p>Universities of Applied Sciences affiliated to the Netherlands Association of Universities of Applied Sciences (Vereniging Hogescholen) (VH, n=35 of 37)<br><br></p> </li> </ul> </li> <li><em>openalex_works_20231223_rpo_nl_2022 </em>- record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022<br><br></li> <li><em>openaire_products_20240116_rpo_nl_2022 - </em>record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022</li> </ul>
Early Cretaceous climate model output from the Kiel Climate Model (from Steinig et al. 2024)
<h1>README</h1> <p>This directory contains climate model output data for 36 different Early Cretaceous (Aptian/Albian) simulations performed with the Kiel Climate Model (KCM; ECHAM5/NEMO). Each zip file contains climatological mean values (monthly mean for the atmosphere; annual mean for the ocean) averaged over the last 100 model years of each simulation in netCDF format. A detailed description of the model setup, boundary conditions and integration strategy is given in the associated publication ("Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis" in Climate of the Past; <a href="https://doi.org/10.5194/egusphere-2023-2732">https://doi.org/10.5194/egusphere-2023-2732</a>). Differences in the paleogeographies are limited to the South Atlantic and Southern Ocean regions, but the output is available globally.</p> <h2>Available Models</h2> <p>Table 1: Overview of available model simulations sorted by the four main boundary condition differences discussed in the associated paper:</p> <table> <tbody> <tr> <th>Model ID</th> <th>Opening Stage</th> <th>CO2</th> <th>Drake Passage Depth</th> <th>Walvis Ridge Depth</th> </tr> </tbody> <tbody> <tr> <td>DC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>C51</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C53</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C54</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C61</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C63</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C64</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>WC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WI1</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI2</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI3</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI4</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI5</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI6</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> </tbody> </table> <h2>Available Variables</h2> <p>Each model zip file contains a <code>means</code> directory with the climatological mean output files for the atmosphere and ocean.</p> <h3>Atmosphere</h3> <p>Files starting with <code><Model ID>_mm_*</code> contain the monthly mean climatologies of 2D atmosphere variables. Each file represent a single variable for the following list of variables:</p> <p>Table 2: Overview of available atmospheric variables. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>aclcov</td> <td>total cloud cover</td> </tr> <tr> <td>evap</td> <td>evaporation</td> </tr> <tr> <td>precip</td> <td>total precipitation</td> </tr> <tr> <td>slp</td> <td>mean sea level pressure</td> </tr> <tr> <td>tau_x</td> <td>zonal wind stress</td> </tr> <tr> <td>tau_y</td> <td>meridional wind stress</td> </tr> <tr> <td>temp2</td> <td>2m air temperature</td> </tr> <tr> <td>tsw</td> <td>surface temperature of water</td> </tr> <tr> <td>uwnd</td> <td>10m u-velocity (winds)</td> </tr> <tr> <td>vwnd</td> <td>10m v-velocity (winds)</td> </tr> </tbody> </table> <h3>Ocean</h3> <p>Ocean variables are split across 4 individual files following the staggered grid of the NEMO ocean model. Naming structure follows <code><Model ID>.mean_grid_{T|U|V|W}.nc</code></p> <p>Table 3: Overview of available ocean variables on the T-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>iowaflup</td> <td>Ice=>ocean net freshwater</td> </tr> <tr> <td>sobowlin</td> <td>Bowl Index</td> </tr> <tr> <td>sohefldo</td> <td>Net Downward Heat Flux</td> </tr> <tr> <td>soicealb</td> <td>Ice Albedo</td> </tr> <tr> <td>soicecov</td> <td>Ice Cover</td> </tr> <tr> <td>soicetem</td> <td>Ice Surface Temperature</td> </tr> <tr> <td>somixhgt</td> <td>Turbocline Depth</td> </tr> <tr> <td>somxl010</td> <td>Mixed Layer Depth 0.01</td> </tr> <tr> <td>sorunoff</td> <td>Runoffs</td> </tr> <tr> <td>sosalflx</td> <td>Surface Salt Flux</td> </tr> <tr> <td>sosaline</td> <td>Sea Surface Salinity</td> </tr> <tr> <td>soshfldo</td> <td>Shortwave Radiation</td> </tr> <tr> <td>sosheig</td> <td>Sea Surface Height</td> </tr> <tr> <td>sosstsst</td> <td>Sea Surface temperature</td> </tr> <tr> <td>sowaflcd</td> <td>concentration/dilution water flux</td> </tr> <tr> <td>sowaflep</td> <td>atmos=>ocean net freshwater</td> </tr> <tr> <td>sowaflsp</td> <td>solid precipitation from atm</td> </tr> <tr> <td>sowafltp</td> <td>total PE flux from atm</td> </tr> <tr> <td>sowaflup</td> <td>Net Upward Water Flux</td> </tr> <tr> <td>vosaline</td> <td>Salinity</td> </tr> <tr> <td>votemper</td> <td>Temperature</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Overview of available ocean variables on the U-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sozotaux</td> <td>Wind Stress along i-axis</td> </tr> <tr> <td>vozocrtx</td> <td>Zonal Current</td> </tr> <tr> <td>vozoeivu</td> <td>Zonal EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 5: Overview of available ocean variables on the V-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sometauy</td> <td>Wind Stress along j-axis</td> </tr> <tr> <td>vomecrty</td> <td>Meridional Current</td> </tr> <tr> <td>vomeeivv</td> <td>Meridional EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 6: Overview of available ocean variables on the W-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>soleaeiw</td> <td>eddy induced vel. coeff. at w-point</td> </tr> <tr> <td>soleahtw</td> <td>lateral eddy diffusivity</td> </tr> <tr> <td>voddmavs</td> <td>Salt Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeavm</td> <td>Vertical Eddy Viscosity</td> </tr> <tr> <td>votkeavt</td> <td>Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeevd</td> <td>Enhanced Vertical Diffusivity</td> </tr> <tr> <td>votkeevm</td> <td>Enhanced Vertical Viscosity</td> </tr> <tr> <td>voverctz</td> <td>Vertical Velocity</td> </tr> <tr> <td>voveeviw</td> <td>Vertical EIV Velocity</td> </tr> </tbody> </table> <p> </p> <p>Publication: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.: Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2732, 2023.</p> <p>Source: This dataset is available via <a href="https://doi.org/10.5281/zenodo.11386835">Zenodo DOI: 10.5281/zenodo.11386835</a>.</p> <p>Authors: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.</p> <p>License: This work is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0 </a>.</p>
3D output of idealized large-eddy simulations with varying speed and surface heating to assess Doppler lidar scan patterns
<p><span>This dataset consists of nine idealized large-eddy simulations that were designed to systematically investigate the ability of different Doppler lidar scan patterns to measure the 3-dimensional wind vector at one point or in one profile. For more information, please see the documentation.</span></p>
ACCESS-AM2 model output for 2017-2018 MARCUS and 2018-2019 CAMMPCAN RSV Aurora Australis voyages
<p>The dataset includes model output from the ACCESS-AM2 model corresponding to the MARCUS (Measurements of Aerosols, Radiation and Clouds over the Southern Oceans) 2017-2018 voyages and the CAMMPCAN (Chemical and Mesoscale Mechanisms of Polar Cell Aerosol Nucleation) 2018-2019 voyages. The MARCUS voyages included a limited number of CAMMPCAN instruments while the CAMMPCAN voyages included the full suite of instruments. </p> <p>The model version used was ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) run with CMIP6 AMIP configuration, nudged with ERA5 reanalysis and full chemistry switched on. The model was configured with a horizontal resolution of 1.25◦ latitude and 1.875◦ longitude and 85 vertical levels. ACCESS-AM2 uses the UK Met Office’s Unified Model Global Atmosphere (UM10.6 GA7.1) as the atmosphere module, the Community Atmosphere Biosphere Land Exchange model version 2.5 (CABLE2.5) as the land-surface module and the Global Model of Aerosol Processes (GLOMAP-mode) as the aerosol module. More information on the ACCESS-AM2 model can be found at <a href="https://doi.org/10.1071/ES19033">https://doi.org/10.1071/ES19033</a>.</p> <p>The data is at daily means spanning 29-10-2017 to 26-03-2018 (149 days) for MARCUS, and 25-10-2018 to 24-03-2019 (151 days) for CAMMPCAN.</p> <p>Files included in this upload include:</p> <ul> <li>aa1718_cg893_track.nc: aerosol, chemistry, and meteorology model data for the MARCUS voyages</li> <li>cg893_daily_mean_MARCUS_size_distributions.nc: calculated aerosol size distribution model data for the MARCUS voyages </li> <li>aa1819_cg893_track.nc: aerosol, chemistry, and meteorology model data for the CAMMPCAN voyages</li> <li>cg893_daily_mean_CC_size_distributions.nc: calculated aerosol size distribution model data for the CAMMPCAN voyages</li> </ul> <p>File names refer to Aurora Australis, voyage years (either 2017-2018 or 2018-2019), followed by the model run and data type.</p> <p>An overview of the variable field names, variable long names, height profile availability and units available in the dataset is provided in VariablesOverview.xlsx. </p> <p>A Jupyter Notebook is also included and contains scripts that can be used to create figures for preliminary analysis using the model data.</p> <p>Additional information:</p> <ul> <li>MARCUS details: <a href="https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf">https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf</a></li> <li>CAMMPCAN details: <a href="https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation">https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation</a></li> <li>MARCUS Observations: <a href="https://doi.org/10.26179/5e54ab5e5d56f">https://doi.org/10.26179/5e54ab5e5d56f</a></li> <li>CAMMPCAN Observations: <a href="https://doi.org/10.26179/5e546f452145d">https://doi.org/10.26179/5e546f452145d</a> </li> </ul> <p>The GitHub repository containing the code used to produce these datasets can be found here: <a href="https://github.com/llamprey/aurora_voyages">https://github.com/llamprey/aurora_voyages</a></p> <p> </p> <p>Versions:</p> <p>1.0.0: Initial Version.</p> <p>1.1.0: Fixed bug where CN and CCN fields were incorrectly calculated.</p> <p>1.2.0: Updated aerosol size distribution files</p>
Turkiye, Regional Domestic Input Output Tables, 2022, 26 Region, 62 X 62 Matris
<p>Regional data is crucial for conducting inter-regional economic analyses, understanding productivity, wages, and minimum wage dynamics, assessing the impact of unforeseen events like natural disasters, comparing sectors, and evaluating development levels. Supply-use tables, employment, and demographic statistics are the most commonly used data sources for these analyses.</p> <p>To address these needs and for my own economic analyses, I have created regional supply-use tables at the province level. Additionally, I have generated domestic input-output tables using the NUTS 2 regional classification for conducting regional and inter-regional analyses in Turkiye.</p> <h2>Scope of the Research</h2> <p>This research presents domestic input-output tables for 62 industries in 26 NUTS 2 regions of Turkiye for the year 2022.</p> <h2>Methodology</h2> <p>Regional accounts are published at the A10 level for every province in Turkey. In the Input-Output tables, which consist of 62 sectors, the input-output distribution has been conducted on a sector basis to derive the total at the A10 level. The estimations at the provincial level are automatically derived from regional accounts data.</p> <p>The NUTS 2 level regional input-output data have been published and disseminated with an A62 matrix. Additionally, province-level input-output data have been published and disseminated with an A62 matrix through the application. After summations the regional input output tables have been estimated.</p> <h2><strong>Application</strong></h2> <p>The link to the relevant application is attached:</p> <h3><a href="https://turkiye.streamlit.app/" target="_blank" rel="noopener ugc nofollow">https://turkiye.streamlit.app/</a></h3> <p> </p>
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