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2,019 results for “boundary”
Likelihood maps of East Antarctic lithospheric domain boundaries.
<p>The first public version of maps generated by the methods described by Stål et al (to submit 2019). </p> <p>Updated with SCons sconstruct file containing all code used for the study. </p>
Data in support of 'The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo'
<p>Data in support of 'Chandler M, Zilberman NV, Sprintall J. (2024). The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo. <em>Journal of Geophysical Research: Oceans</em>. <a href="https://doi.org/10.1029/2024JC021098" target="_blank" rel="noopener">https://doi.org/10.1029/2024JC021098</a>'</p> <p>There are 4 netCDF files:</p> <ol> <li>swpb_dwbc_deep_argo_profiles_chandler2024.nc</li> <li>swpb_dwbc_deep_argo_trajectories_chandler2024.nc</li> <li>kt_dwbc_deep_argo_time_series_chandler2024.nc</li> <li>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</li> </ol> <p><strong>swpb_dwbc_deep_argo_profiles_chandler2024.nc </strong>contains the delayed-mode profiles of potential temperature and salinity on a 10-dbar pressure grid from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[pressure; latitude; longitude; time; wmo_id; theta; salinity]</em></p> <p><strong>swpb_dwbc_deep_argo_trajectories_chandler2024.nc </strong>contains delayed-mode trajectories from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[latitude; longitude; u; v; pressure; wmo_id; time]</em></p> <p><strong>kt_dwbc_deep_argo_time_series_chandler2024.nc</strong> contains the 2021--2022 monthly time series of dynamic height, salinity, and potential temperature between 2000--4000-dbar computed from the spatially-averaged Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[time; pressure; theta; salinity; dh; region_long; region_lat]</em></p> <p><strong>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</strong> contains seasonal cycles of dynamic height, salinity, and potential temperature (including the decomposition into heave/spice) between 2000--4000-dbar from the Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[pressure; theta; theta_heave; theta_spice; salinity; dh; region_long; region_lat]</em></p> <p>Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (<a href="https://argo.ucsd.edu/" target="_blank" rel="noopener">https://argo.ucsd.edu/</a>). The Argo Program is part of the Global Ocean Observing System. A full list of acknowledgements can be found in the affiliated <a href="https://doi.org/10.1029/2024JC021098">publication</a>.</p> <p><code>Version history:</code><br><code>v1.0 First created (06-March-2024)</code><br><code>v1.1 Updated to include accepted publication reference (15-October-2024)</code></p>
Planetary Boundaries Assessment of Flue Gas Valorization into Ammonia and Methane
<p>Dataset associated with the publication "Planetary Boundaries Assessment of Flue Gas Valorization into Ammonia and Methane" by Sebastiano C. D'Angelo, Julian Mache, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1021/acssuschemeng.1c01915">https://doi.org/10.1016/B978-0-323-95879-0.50134-X</a>. The dataset includes the numeric data associated with Table 1 and Figure 2, converted into a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>StreamTable</strong>: numerical values associated with full set of streams depicted in Figure 1, among which a selection is reported in Table 1.</li> <li><strong>LCA-Results</strong>: numerical values associated with the breakdown of the environmental impacts for the selection of scenarios reported in Figure 2, for all the assessed control variables.</li> </ul>
Planetary Boundaries Analysis of Low-Carbon Ammonia Production Routes
<p>Dataset associated with the publication "Planetary Boundaries Analysis of Low-Carbon Ammonia Production Routes" by Sebastiano C. D'Angelo, Selene Cobo, Abhinandan Nabera, Antonio J. Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1021/acssuschemeng.1c01915">https://doi.org/10.1021/acssuschemeng.1c01915</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript and in the Supporting Information (SI), as well as the tables presented in the SI converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>LCA-Total</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. The results are presented for the three different downscaling approaches considered in the study. The global warming impacts for all the scenarios, calculated with the ReCiPe 2016 methodology (hierarchist approach), are here reported, as well.</li> <li><strong>LCA-Breakdown</strong>: numerical values associated with the breakdown of the environmental impacts for the selection of scenarios reported in the main manuscript, for all the assessed control variables.</li> <li><strong>Economics</strong>: numerical values associated with the breakdown of the economic impacts reported in the main manuscript, for all the assessed scenarios.</li> <li><strong>SI-Tables-LCI</strong>: tables reported in the SI associated with the environmental assessment of all the scenarios.</li> <li><strong>SI-Tables-Economics</strong>: tables reported in the SI associated with the economic assessment of all the scenarios.</li> </ul>
Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport
<p>Dataset associated with the publication "Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport" by Margarita A. Charalambous, Juan D. Medrano-Garcia, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1016/B978-0-323-85159-6.50328-6">https://doi.org/10.1016/B978-0-323-85159-6.50328-6</a>. The dataset includes the numeric data required to plot all the figures embedded in the manuscript.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>LCA-Inventories:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of FT-diesel from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant, as well as, the production of hydrogen from biomass and polymer electrolyte water electrolysis. Moreover, required adjustments to accommodation FT-diesel fuel in the truck transport activity are summarized.</li> <li><strong>LCA-Total</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2.</li> <li><strong>LCA-Breakdown</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for three control variables (CO<sub>2</sub> concentration, and biosphere integrity). These values represent the data used to create Figure 3. </li> </ul>
The role of hydrogen in heavy transport to operate within planetary boundaries
<p>Dataset associated with the publication "The role of hydrogen in heavy transport to operate within planetary boundaries" by Antonio Valente, Victor Tulus, Galán-Martín, Mark A. J. Huijbregts, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D1SE00790D">https://doi.org/10.1039/D1SE00790D</a>. The dataset includes the numeric data associated with the plots described in the main manuscript, as well as the tables presented in the main manuscript converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>Tables</strong>: tables 3 and 4, as reported in the main manuscript, with evolution considered for the main technical parameters and the values of the parameters used in the baseline, best and worst scenario.</li> <li><strong>Plots</strong>: numerical values associated with figures 2, 3, and 4, as reported in the main manuscript.</li> </ul>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
Hubbard Brook Experimental Forest Boundary: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The experimental forest boundary was manually digitized. The eastern boundary was truncated by a new boundary delineated on a paper diazo copy of the Hubbard Brook Watershed Map supplied by Wayne Martin of the USFS. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
GIS00 GIS Coverages Defining the Site Boundary of Konza Prairie (1977-present)
This dataset contains the boundary polygon of the Konza Prairie Biological Station (KPBS). Data type one (GIS000) defines the original KPBS boundary used from 1977 until 1982, type two contains the extended boundary from 1982 (GIS001) to 1997, and type three (GIS002) contains the boundary since 1997. These data are available as zipped (.zip) shapefiles (.shp).
GIS01 GIS Coverages Defining Internal Boundaries of Konza Prairie (1977-present)
This dataset defines the internal boundaries of the Konza Prairie Biological Station (KPBS). Data type one (GIS010) is a record of all fenced areas on KPBS with GIS011 providing locations for all gates and type of gate (exterior, bison, and cattle). Data type three (GIS012) represents various large-scale research areas on Konza including bison grazed, cattle grazed, fire reversal, etc. These data are available as zipped (.zip) shapefiles (.shp).
El Verde Field Station (EVFS) Area Boundary KML shapefile and coordinates
The EVFS Area Boundary shapefile (.kml) and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Characteristics of the Marsh-Forest Boundary within Chesapeake Bay Region Coastal Watersheds
Sea level rise is leading to the rapid landward migration of marshes into coastal forests and other terrestrial ecosystems. Although complex biophysical interactions likely govern these ecosystem transitions, projections of sea level driven land conversion commonly rely on a simplified delineation of the marsh-upland boundary based on tidal datums alone. To determine the influence of biophysical drivers on the elevation of the marsh-forest transition, and their implication for land conversion, we examined almost 100,000 high-resolution marsh-forest boundary elevation points, determined independently from tidal datums, alongside 14 environmental variables in the Chesapeake Bay, the largest estuary in the United States.
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Modelling of Stably Stratified Atmospheric Boundary Layers with Varying Stratifications
<p>This repository contains data that was used for publishing article called <a href="https://link.springer.com/article/10.1007%2Fs10546-020-00527-8"><em>Modelling of Stably Stratified Atmospheric Boundary Layers with Varying Stratifications</em></a>. The repository compliments the publication in the sense that it provides qualitative insight for comparison and exploration.</p> <p><strong>Keywords</strong>: GABLS1, Open data, Stably-stratified turbulence, Turbulence parametrization</p> <p>The data is stored inside sixteen files. The file names are split into a part that describes variables and part that describes simulation. Here's an example of a file name:</p> <p>budgets.cr0375.csv</p> <p>The first part <em>budgets</em> refers to variables inside the file and the second part <em>cr0375</em> refers to forcing conditions (in this example cooling rate of 0.375 Kelvin per hour) used in the simulation.</p> <p><strong>Variables</strong>:</p> <ul> <li>mean wind speed and mean potential temperature (<em>first_order_stat</em>)</li> <li>variance and covariance variables that describe turbulence properties (<em>second_order_stat)</em></li> <li>variables in the turbulent kinetic energy and half the temperature variance equations <em>(budgets</em>)</li> <li>contain values for model coefficients that can be used for calculating second order statistics <em>(lambda_beta_coeffs)</em></li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>cooling rate at the surface 0.25 Kelvin per hour <em>(cr025)</em></li> <li>cooling rate at the surface 0.375 Kelvin per hour <em>(cr0375)</em></li> <li>cooling rate at the surface 0.5 Kelvin per hour (<em>cr05)</em></li> <li>cooling rate at the surface 1.0 Kelvin per hour <em>(cr1)</em></li> </ul> <p><strong>Note</strong>: The results presented in the repository are taken after the ninth hour of the simulation while the results in the published paper is averaged between the eight and ninth hour. This difference should be negligible.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Magnetism of Topological Boundary States Induced by Boron Substitution in Graphene Nanoribbons
<p>OPEN DATA related to the research publication:</p> <p>Niklas Friedrich, Pedro Brandimarte, Jingcheng Li, Shohei Saito, Shigehiro Yamaguchi, Iago Pozo, Diego Peña, Thomas Frederiksen, Aran Garcia-Lekue, Daniel Sánchez-Portal, and José Ignacio Pascual, <em>Magnetism of Topological Boundary States Induced by Boron Substitution in Graphene Nanoribbons</em>, Phys. Rev. Lett. <strong>125</strong>, 146801 (2020) [arXiv:2004.10280]</p> <p>Abstract: Graphene nanoribbons (GNRs), low-dimensional platforms for carbon-based electronics, show the promising perspective to also incorporate spin polarization in their conjugated electron system. However, magnetism in GNRs is generally associated with localized states around zigzag edges, difficult to fabricate and with high reactivity. Here we demonstrate that magnetism can also be induced away from physical GNR zigzag edges through atomically precise engineering topological defects in its interior. A pair of substitutional boron atoms inserted in the carbon backbone breaks the conjugation of their topological bands and builds two spin-polarized boundary states around them. The spin state was detected in electrical transport measurements through boron-substituted GNRs suspended between the tip and the sample of a scanning tunneling microscope. First-principle simulations find that boron pairs induce a spin 1, which is modified by tuning the spacing between pairs. Our results demonstrate a route to embed spin chains in GNRs, turning them into basic elements of spintronic devices.</p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Dataset of publication "Investigation of the discharge coefficient in the laminar boundary layer regime of critical flow Venturi nozzles calibrated with different gases including hydrogen"
<p>The attached files contain experimental raw data and fluid properties for the nozzles 1 and 2 mentioned in the paper. The data in the attached files can be used to calculate the Cd values published in the paper.</p>
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
Datasets for "Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals"
<p>This repository contains raw data for the paper “Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals”. It contains the input files, scripts, and raw data of the simulations, as well as raw data from scanning transmission electron microscopy.</p>
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.