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71 results for “post-processing”
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine: otolith data, model data, and post-processed model data products
This dataset includes hatch and larval period for sand lance collected in 2019 and results from particle tracking runs of simulated sand lance larvae throughout the Northeast U.S. Shelf as part of Long-Term Ecological Research (NES-LTER). Release dates vary by region, corresponding to hatch and settlement dates of settling sand lance collected in 2019. Particles were depth-keeping throughout the upper 40 m to best replicate our understanding of the vertical distribution of sand lance larvae. Data were used to determine the average particle transport pathways from these sand lance habitats, including connectivity among the three hotspots, and spatial variability of connectivity within each hotspot. Further information can be found within the manuscript: Suca, J. J., Ji, R., Baumann, H., Pham, K., Silva, T. L., Wiley, D. N., Feng, Z., & Llopiz, J. K. (2022). Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine. Fisheries Oceanography, 31( 3), 333-352. https://doi.org/10.1111/fog.12580
OSeaIce post-processed data
<p>These are the EC-Earth3 post-processed data that can be used with the associated Python scripts (https://doi.org/10.5281/zenodo.4291971) in order to produce the figures of the paper that summarizes the results arising from the sensitivity experiments carried out in the framework of the EU Horizon 2020 OSeaIce project (Marie Slodowska-Curie project, grant agreement no. 834493):</p> <p>Docquier et al. (2020). Impact of ocean heat transport on the Arctic sea-ice decline: A model study with EC-Earth3. Accepted in Climate Dynamics.</p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
NLDAS-2 Sacramento (SAC) Post-processed Daily-mean Soil Moisture Data
<p>This dataset contains North American Land Data Assimilation System Version 2 (NLDAS-2) Sacramento (SAC) post-processed daily-mean soil moisture data from 1993 to 2017 at four layers: 0-10 cm, 10-40 cm, 40-100 cm, and 0-100 cm. Original post-processing of the hourly data was conducted at National Oceanic and Atmospheric Administration (NOAA) by Dr. Youlong Xia and then later provided to the NOAA Physical Sciences Laboratory (PSL). Daily-mean data were generated at PSL from the hourly data by averaging data from 8 times each day (0,3,6,9,12,15,18,21 Z). The data are written in netCDF format and provided in annual netCDF files.</p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
McCulloch et al 2022 UM post-processed Mars dataset
<p>Supplementary dataset and Jupyter notebook for preproduction of UM data within figures presented in McCulloch <em>et al.,</em> 2022. </p> <p>Data is a post-processed extract of the raw dataset for each variable. Data from the raw dataset has been extracted according to the appropriate Martian month, zonally meaned and converted to a <span class="math-tex">\(\sigma\)</span>/pressure coordinate system. This process is the same as is applied to the MCD dataset, which can be seen in the Jupyter notebook.</p> <p>The notebook provides the code needed to reproduce the figures with the given data. All instructions are detailed within the notebook, including package dependencies and configuration options. Due to licensing, we are only able to provide access to the UM post-processed data, for the MCD dataset please follow the instructions within the notebook.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset
<p><strong>Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset</strong><br> </p> <p>We curated a dataset of post-processed <em>in situ</em> evapotranspiration (ET) measurements, primarily from eddy covariance flux towers, from stations located within the contiguous United States. The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data that were post-processed from 148 flux towers, 4 weighing lysimters, and 8 Bowen Ration stations. Original data was retrieved from the <a href="https://ameriflux.lbl.gov/">AmeriFlux</a> network and other networks and partners. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. Energy balance components (latent and sensble heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and latent energy (ET) was subject to additional visual quality control. Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and more.</p> <p><br><strong>Description of the Data and file structure</strong></p> <p>The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within: </p> <p>1. A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.<br>2. A similar collection of time series files for monthly aggregated data in "monthly_data_files". <br>3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory. <br>4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, land cover type, principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units. </p> <p>Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file. </p> <p>Here is a visual of the file structure:</p> <blockquote> <p><br>flux_ET_dataset<br>│ README.md<br>│ variable_explanation.xlsx<br>│ station_metadata.xlsx<br>│<br>└───daily_data_files<br>│ │ [site ID]_daily_data.csv<br>│ │ ...<br>└───monthly_data_files<br>│ │ [site ID]_monthly_data.csv<br>│ │ ...<br>└───graphical_files<br>│ │ [site ID]_plots.html<br>│ │ ...<br>```</p> </blockquote> <p>The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m<sup>2</sup>. </p> <p><br><strong>Sharing/access Information</strong></p> <p>Currently, this repository is the only location where the data are hosted. Original data, prior to post-processing, were retrieved from multiple providers listed below:</p> <p>* AmeriFlux network (https://ameriflux.lbl.gov/) </p> <p>* California State University, Monterey Bay, Seaside, CA, USA </p> <p>* Desert Research Institute, Reno, NV, USA </p> <p>* gridMET, Northwest Knowledge Network at the University of Idaho (https://thredds.northwestknowledge.net/) </p> <p>* United States Geological Survey Nevada Water Science Center, Carson City, NV, USA </p> <p>* Delta-Flux network, Arkansas, Louisiana, MS, USA </p> <p>* United States Department of Agriculture Agricultural Research Service (USDS-ARS): </p> <p> * Sustainable Water Management Research Unit, Stoneville, MS, USA </p> <p> * US Salinity Laboratory, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, USA </p> <p> * Conservation & Production Research Laboratory, Bushland, TX, USA </p> <p> * US Arid-Land Agricultural Research Center, Maricopa, AZ, USA </p> <p> * Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA </p> <p>Further contact information for each station as well as DOI's for original AmeriFlux data are included in the "station_metadata.xlsx" file. </p> <p><br><strong>Code/Software</strong></p> <p>All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.1.6. The package is hosted on <a href="https://github.com/Open-ET/flux-data-qaqc">GitHub</a> and <a href="https://pypi.org/project/fluxdataqaqc/">PyPI</a>, it also has <a href="https://flux-data-qaqc.readthedocs.io/en/latest/">online documentation</a> including an in depth user tutorial. </p>
Raw and post-processed data for the study of prosodic cues to word boundaries in a segmentation task using reverse correlation
<p>The current dataset provides all the stimuli (folder <strong>../01-Stimuli/</strong>), raw data (folder <strong>../02-Raw-data/</strong>) and post-processed data (<strong>../03-Post-proc-data/</strong>) used in a prosody reverse correlation study with the title "prosodic cues to word boundaries in a segmentation task using reverse correlation" by the same authors. The listening experiment was implemented using one-interval trials with target words of the structure l'aX (option 1) and la'X (option 2). The experiment was designed and implemented using the <a href="https://github.com/aosses-tue/fastACI">fastACI toolbox</a> under the name 'segmentation'. A between-subject design was used with a total of 47 participants, who evaluated one of five conditions, LAMI (N=16), LAPEL (N=18), LACROCH (N=5), LALARM (N=5), and LAMI_SHIFTED (N=3). More details are given in the related publication (to be submitted to JASA-EL in May 2023).</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
CMIP5 and CMIP6 post-processed AMOC and MLD data supporting Jesse et al. 2023
<p>These are the datasets supporting the paper "Why is CMIP6 projecting larger ocean dynamic sea level in the North Sea than CMIP5?" from Jesse et al. submitted to ERL.</p> <p>See the paper for more information about the data and GitHub for the code that generated the data: https://github.com/dlebars/CMIP_SeaLevel</p> <p>The Atlantic meridional overturning circulation (AMOC) is computed at two latitudes 26N and 35N from different CMIP variables:</p> <p>cmip5_amoc is computed from the variable "msftmyz".</p> <p>cmip6_amoc is computed from the variable "msftmz" or "msftyz" as indicated in the name of the file.</p> <p>cmip5_amoc_vo and cmip6_amoc_vo are computed from the meridional ocean velocity ("vo").</p> <p>For mixed layer depth the variable "mlotst" is used.</p> <p> </p>
Chlorophyll and phytoplankton composition climatological data on the Northwest Atlantic Shelf from 1978 to 2014: post-processed model data
This dataset includes 8-day composite of surface chlorophyll and bimonthly phytoplankton size composition climatological results on the Northwest Atlantic Shelf from the Gulf of Maine to the Mid-Atlantic Bight based on the physical-biological coupled model results from 1978 to 2014. Two size classes, small phytoplankton (SP) and large phytoplankton (LP), are provided. For more details please see: Zhengchen Zang, Rubao Ji, Zhixuan Feng, Changsheng Chen, Siqi Li, and Cabell S Davis (2021) Spatially varying phytoplankton seasonality on the Northwest Atlantic Shelf: a model-based assessment of patterns, drivers, and implications. ICES Journal of Marine Science, Volume 78, Issue 5, 1920-1934, https://doi.org/10.1093/icesjms/fsab102.
HarmonEPS modified routines, post-processing scripts and example data used in Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van der Veen, S. and Barkmeijer, J., An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review
<p>This dataset contains:</p> <p>- Modified code routines/configurations files used in the EPS of Harmonie-Arome (HarmonEPS) CY43H2.2 version of the model.</p> <p>- Verification scripts from the HARP verification tool, used to verify model output against SYNOP observations.</p> <p>- Post-processing and plotting scripts in python, used in the manuscript.</p> <p>- A subset of the data produced by this study as example input in the verification and post-processing/plotting scripts.</p> <p>This dataset is used in:</p> <p>~Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van Der Veen, S. and Barkmeijer, J. An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review</p>
Calculation of RF sheath properties from surface wave-fields: a post-processing method
<p>The accompanying files contain digital data for figures in the article "Calculation of RF sheath properties from surface wave-fields: a post-processing method" by J.R. Myra and H. Kohno, submitted to the journal Plasma Physics and Controlled Fusion.</p> <p><br> Abstract:</p> <p>In ion cyclotron range of frequency (ICRF) experiments in fusion research devices, radio frequency (RF) sheaths form where plasma, strong RF wave fields and material surfaces coexist. These RF sheaths affect plasma material interactions such as sputtering and localized power deposition, as well as the global RF wave fields themselves. RF sheaths may be modeled by employing a sheath boundary condition (BC) in place of the more customary conducting wall BC; however, there are still many ICRF computer codes that do not implement the sheath BC. In this paper we present a method for post-processing results obtained with the conducting wall BC. The post-processing method produces results that are equivalent to those that would have been obtained with the RF sheath BC, under certain assumptions. The post-processing method is also useful for verification of sheath BC implementations and as a guide to interpretation and understanding of the role of RF sheaths and their interactions with the waves that drive them.</p> <p> </p>
M81 post-processed results from Lightning SED fitting
<p>The post-processed results from the Lightning SED fitting code as fit by the authors for the spatially resolved map of M81. The results are for the MCMC sampler (m81_map_mcmc.fits.gz) and the MPFIT minimizer (m81_map_mpfit.fits.gz). Fitting of this example was performed on a 32-core node of the Pinnacle cluster at the Arkansas High Performance Computing Center.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
ScienceDex guides
Understand access before you commit
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.