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5 results for “surface complexation modelling”
Supplementary files for the article "Reconstruction of the surface temperature employing models with different complexity levels: a study for the mid-Holocene."
<p>Dear members of the scientific community,</p> <p>This dataset contains all data and scripts used to prepare the manuscript "Reconstruction of the Surface Temperature Employing Models with Different Complexity Levels: A Study for the Mid-Holocene." </p> <p>Notice that the compressed file contains folders for the mid-Holocene and pre-industrial scenarios and the climatologies of the scenarios' differences. To sum up, we provide each model output adopted in this study and their ensembles: high-complexity models (HCM), Reduced-complexity models (RCM), and All-complexity models (ACM). The statistics and plots can be generated by running the R scripts within the "R_script" folder.</p> <p><br>Best regards,</p> <p>Emerson D. Oliveira</p>
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
dataset for Machine Learning Surrogates for Surface Complexation Model of Uranium Sorption to Oxides
<p>The data sets are used to train, validate, and test SCM surrogates for uranium sorption on oxide surfaces. </p> <p>train_norm.csv, val_norm.csv, and test_norm.csv are used to train, validate, and evaluate DNN-SCM surrogate. </p> <p>test_set1.csv and test_set2.csv are two additional datasets to evaluate the model's performance. </p>
CESM2 data for "Ocean complexity shapes sea surface temperature variability in a CESM2 coupled model hierarchy" - submitted to JCLI
<p><strong>CESM2 Experiment names:</strong></p> <ul> <li>FC = fully coupled model, CESM2 (variables freely available on https://esgf-node.llnl.gov/search/cmip6/)</li> <li>MD = mechanically decoupled model, CESM2</li> <li>SOM = slab ocean model, CESM2</li> </ul> <p>All datasets are for pre-industrial forcing (e.g., piControl), nominal 1-degree horizontal resolution </p> <p>---</p> <p>Decoding the files names:</p> <ul> <li><strong>climatology_monthly </strong>= 12 month climatology </li> <li><strong>climatology_annual</strong> = time mean climatology</li> <li><strong>variance</strong> = anomaly variance computed over time</li> </ul> <p>---</p> <p>Variables:</p> <ul> <li><strong>PRECL</strong> = large-scale convective precipitation</li> <li><strong>PRECC</strong> = convective precipitation</li> <li><strong>total precipitation (not provided but can be calculated)</strong> = PRECC + PRECL</li> <li><strong>HMXL</strong> = mixed layer depth</li> <li><strong>SST</strong> = sea surface temperature </li> </ul> <p><strong>Files for the CESM2 MD piControl run:</strong></p> <ol> <li>forcing_coupled.F90: POP2 (ocean) source code changes for cesm2.1.4-rc08 (search for "slarson" throughout code to find our changes</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUX.nc: 6 hourly climatology for TAUX, from a FC run of CESM2. This file and the TAUY climatology are opened and read in the "rotate wind stress" subroutine in forcing_coupled.F90. This file is named "x2oavg_Foxx_taux_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code)</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUY.nc: 6 hourly climatology for TAUY. This file is named "x2oavg_Foxx_tauy_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code) </li> </ol> <p> </p>
Deep Neural Network Surrogate for Surface Complexation Model of Metal Oxide/Electrolyte Interface
<p>These files are the data used in the paper "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ncAYQ4MAAAAJ&sortby=pubdate&citation_for_view=ncAYQ4MAAAAJ:LkGwnXOMwfcC">Deep neural network surrogate for surface complexation model of metal oxide/electrolyte interface</a>".</p> <ul> <li>CSV files are used to train the DNN model.</li> <li>NPZ files are used to train the random forest model. </li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.