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79 results for “modifier model”
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>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
MAR-M-247 creep assessment through a modified theta projection model - Figures 2 and 5
<p>These two programs provide a way to rebuild the MAR-M-247 creep data presented in the paper:</p> <p>G. Maggiani, M.J. Roy, S. Colantoni, P.J. Withers, MAR-M-247 creep assessment through a modified theta projection model, Materialia, Volume 7, 2019, 100392, ISSN 2589-1529, https://doi.org/10.1016/j.mtla.2019.100392. http://www.sciencedirect.com/science/article/pii/S2589152919301887)<br> </p> <p>In Paper_Figure_2.m two coefficients of the paper itself are corrected and a comparison with what written in the paper and the corrected value is provided. One typo error for theta 1 at 982°C and 140 MPa where 6.9 must be 1.9. The other is for 1038°C 50 MPa theta4. In the paper it is written e^-11 while it actually should have been e^-10.</p> <p>Paper_Figure_5.m more decimal values are provided for the coefficients a, b, c and d that are used to rebuild the theta values.</p> <p> </p> <p> </p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
A Modified Doyle-Fuller-Newman Model Enables the Macroscale Physical Simulation of Dual-ion Batteries - Dataset and Software
<p>This dataset contains:</p> <p>- all the raw cycling data of the three-electrode cell used to gather the experimental data for the model validation (VMP data, exported with EC-LAB);<br>- the specific, processed data used in the model validation step (0.2C discharge, 5C discharge, EIS data);<br>- the COMSOL dual-ion battery model (version 6.0). IMPORTANT: activate the "Electric potential at the positive electrode current collector (only for EIS)" boundary condition when simulating impedance spectroscopy, and deactivate it when simulating charge/discharge curves; the charge-discharge profile can be modified by changing the duration of the test, the C-rate, and the conditions set in the "Events" section.</p> <p>Update: Fixed the model to work also in the 6.2 version of COMSOL (Substituted Dleff with Dleffxx in the modified weak expression of the cathode mass conservation equation). Download the new version!</p>
Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</p>
More atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain atmospheric climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format (subsequently compressed), with fields described within those files.</p>
Modified Ecosystem Demography (ED2) model with sagebrush (shrub) parameters
<p># Original Ecosystem Demography (ED2) model was developed by Moorcroft et al., 2001 and Medvigy et al., 2009, and team, which is available at github (https://github.com/EDmodel/ED2).</p> <p># This repository contains modified Ecosystem Demography (ED2) model for sagebrush (shrub) PFT. In order to do so we primarily updated ed_params.F90 with additional PFT, which is represented by number '18'. For the initial works, we selected allometrics equations for temperate PFTs in ED2 to develop coefficients (parameters) related to allometric relationships (such as dbh to height , dbh to leaf biomass etc). We used cube root of volume to represent diameter to calculate allometric equations for shrubs, since they do not have clear main trunk like trees. Another source code ed_max_dims.F90 was also changed to update PFT dimensions. </p> <p># This repository also includes namelist (ED2IN) file used to run sensitivity and optimization of sagebrush parameters. </p> <p>References</p> <p>Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft, P. R.: Mechanistic scaling of ecosystemfunction and dynamics in space and time: Ecosystem Demography model version 2, J. Geophys. Res., 114, G01002, doi:10.1029/2008JG000812, 2009.</p> <p>Moorcroft, P. R., Hurtt, G. C., Pacala, S. W.: A method for scaling vegetation dynamics: the Ecosystem Demography model (ED), Ecol. Monogr., 71, 557–586, <a href="https://doi.org/10.1890/0012-9615(2001)071%5b0557:AMFSVD%5d2.0.CO;2">https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2</a>, 2001.</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
cross sections modified from overthrust model
<p>The dataset includes three files. The ZK.vtk and Ti2.vtk are the virtual boreholes and cross-sections modified from the overthrust model (https://wiki.seg.org/wiki/SEG/EAGE_Salt_and_Overthrust_Models), respectively, which can be operated by Paraview (https://www.paraview.org/). The color_overthrust.json is the color map file for these two vtk files.</p>
Some atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain 2-D fields of atmospheric T and P maps, as climatologies averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, and from the CESM Large Ensemble control simulation (LENS), the basis for two of the plots in our manuscript that is, as of January 2019, in review at JAMES. All of these data files are binary, written sequentially, 288 x 192. Also included are some PowerPoint-generated pdf images of the two fields.</p>
Model source code, modified model code, and all scripts of the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".
<p>Model source code, modified model code, and all scripts of the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".</p>
Dentine Tubule Occlusion Assessment in a Modified in Situ Model
ClinicalTrials.gov study NCT02768194. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Natural disease history of a canine model of oligogenic RPGRIP1-cone-rod dystrophy establishes variable effects of previously and newly mapped modifier loci
Open the record for dataset details and reuse information.
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Data for "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model"
<p>Model output data for the manuscript "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model", including convective precipitation (PRECC), large-scale precipitation (PRECL), mean SST and U850, <span>column-integrated MSE tendency anomalies, and boundary layer moisture convergence. </span></p>
Analysis Result of Downward Infiltration Experiments Using the Modified Green Ampt Model
<p>The excel "Analysis result of exprement.xlsx" collects the analysis result of downward infiltration experiments using Modified Green Ampt Model (MGAM). The MGAM is built based on the study by Hsu, S.-Y., & Hilpert, M. (2011). Incorporation of dynamic capillary pressure into the Green–Ampt model for infiltration. <em>Vadose Zone Journal, 10</em>(2), 642-653. (<a href="https://doi.org/10.2136/vzj2010.0069">https://doi.org/10.2136/vzj2010.0069</a>).</p> <p>The excel content includes the fitting parameter values, root mean square of the simulations, and simulated initial velocity. Also, the excel file provides the likely maximum capillary rising heights which were estimated based on further capillary rising experiments. </p>
A Modified Guggenheim-Anderson-Boer Model for Analyzing Water Sorption in Coal
<p><strong>Data for the manuscript :A Modified Guggenheim-Anderson-Boer Model for Analyzing Water Sorption in Coal</strong></p>
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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.