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141 results for “ensemble model”
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
Data and code for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.
<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p> </p>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America
<p>The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021). We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper. </p> <p>Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. <em>The Cryosphere, 15</em>(2), 771-791.</p> <p>Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)</p>
Standard Error Estimates from ARRI ensemble GAM model outputs
<p><strong>Summary (Purpose)</strong></p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p>Reference:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114</p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
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Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
Models from: Exploring ensemble applications for multi-sequence myocardial pathology segmentation
<p>Trained models for the MyoPS2020 challenge http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/MyoPS20/</p> <p>As described in the publication: "Exploring ensemble applications for multi-sequence myocardial pathology segmentation"</p> <p>Source code available at: https://github.com/chfc-cmi/miccai2020-myops</p>
Results of ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century", published in The Cryosphere, <a href="https://tc.copernicus.org/articles/14/3033/2020/">https://tc.copernicus.org/articles/14/3033/2020/</a></p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and ISMIP6 Antarctica Projections can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>About the dataset:</p> <p>- The results are based on model output computed from the ISMIP6 native grids that vary between models. <br> - The results are calculated over the ice-covered area of Antarctica, corrected for map projection errors, ice sheet model specific densities taken into account.<br> - Results for the experiments 'exp*' are provided both as raw results and calculated as differences to the control experiment (ctrl_proj_open or ctrl_proj_std depending on the experiment). The later files are named with "minus_ctrl_proj" to indicate that the control run is substracted.<br> - Results for ctrl_proj_open, ctrl_proj_std, hist_open and hist_std are not corrected to remove the control run.</p> <p><br> ------------------------------------------------</p> <p>Directory structure:</p> <p>groupname1<br> modelname1<br> expid<br> computed_iareafl_AIS_groupname1_modelname1_expid.nc<br> computed_iareafl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smb_AIS_groupname1_modelname1_expid.nc<br> computed_smb_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> ...</p> <p>-------------------------------------------------</p> <p><br> Description of variables:</p> <p>icearea - ice area [m^2]<br> iareafl - floating ice area [m^2]<br> iareagr - grounded ice area [m^2]<br> ivol - ice volume [m^3]<br> ivaf - ice volume above floatation [m^3]<br> smb - spatially integrated surface mass balance [kg/s]<br> smbgr - spatially integrated surface mass balance over grounded ice [kg/s]<br> bmbfl - spatially integrated basal melt rate under floating ice (negative for melting ice) [kg/s]</p> <p>Variables per file:</p> <p>rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, in years</p> <p>[variable] - global variable integrated over the Antarctica ice sheet<br> [variable]_region_1 - variable integrated over West Antarctica<br> [variable]_region_2 - variable integrated over East Antarctica<br> [variable]_region_3 - variable integrated over the Antarctic Peninsula<br> [variable]_sector_X - variable integrated over the X sector of the Antarctic ice sheet (18 sectors, from 1 to 18)</p> <p>--------------------------------------------------</p> <p><br> Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Supplementary video content for "Three-dimensional visualization of ensemble weather forecasts", Parts 1 and 2 (Geoscientific Model Development, 2015)
<p>Supplementary video content (full resolution) for the papers "Three-dimensional visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)" and "Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns", Geoscientific Model Development, 2015. The corresponding papers can be found on http://geosci-model-dev.net/.</p>
Proper modelling of ligand binding requires an ensemble of bound and unbound states
<p>Crystallographic data for structures described in the manuscript "Proper modelling of ligand binding requires an ensemble of bound and unbound states".</p>
Supplemental Movie Files for "Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis" by Chriss et al.
<p>This set of Supplemental Information contains two movies made from the simulations from the model developed in the manuscript "<strong>Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis</strong>" by A. Chriss, G. V. Börner, and S. D. Ryan. </p> <p> </p> <p>Supplemental Movie S1: <strong>WT Chromosome Trajectories during Prophase I. </strong>The first file "movie_WT..." contains the file for the results of simulations for the wild-type chromosomes and the exact parameter values can be found in Table 1 of the manuscript. The movie shows one realization of the agent-based model. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S2: <strong>WT Chromosome Trajectories during Prophase I with active dumbbell model. </strong> The second file "movie<em>WT</em>_activedumbbell..." contains the file for the results of the simulations for the modeling of chromosomes as active dumbbells (from polymers) to allow for the study of the effects of elongation, orientation, and flexibility. The movie shows one realization of the agent-based active dumbbell model which is closer to modeling a chromosome as a polymer. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii, but are allowed to change in time as the two beads expand and contract. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S3: <strong><em>spo11</em> hypomorph (30% WT DSB levels) Chromosome Trajectories during Prophase I (parameters from Fig 7B)</strong>. The third file "movie_spo11..." contains the file for the results of the simulations for the spo-11 hypomorph and the associated parameter values can be found in Table 1 of the manuscript. The movie depicts one realization of the agent-based model for the {\it spo11} hypomorphic mutant. The simulation movie covers the homology search process from <em>t = 3h</em> to <em>t = 9h</em> where mutant <em>spo11</em> is associated with a weaker attractive and repulsive force (e.g., reduction to 77% of WT values). True chromosome lengths are incorporated and scale the relevant interaction radii. Matching colors correspond to homologous pairs. The radii represent the homologous attractive and the non-homologous repulsive region. Note that the reduction in interaction strength delays homologous pairing consistent with experimental observations in [13]. </p> <p> </p> <p> </p> <p>The codes that generated these movies were written in Matlab and freely available via GitHub: <a href="https://github.com/sdryan/ChromosomeDynamicsProphase1">https://github.com/sdryan/ChromosomeDynamicsProphase1</a></p> <p> </p> <p>For questions please contact the corresponding authors: G. Valentin Börner <a href="mailto:g.boerner@csuohio.edu">g.boerner@csuohio.edu</a> (Biology) or Shawn D. Ryan <a href="mailto:s.d.ryan@csuohio.edu">s.d.ryan@csuohio.edu</a> (Math).</p>
Model checkpoints for "SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models"
<p>Checkpoints for all SEEDS models in the paper <a href="https://arxiv.org/abs/2306.14066" rel="nofollow">https://arxiv.org/abs/2306.14066</a>, including all SEEDS-GEE and SEEDS-GPP models in the main manuscript, and the additional models trained in the Supplemental Material.</p> <p>Checkpoint naming convention:</p> <ul> <li><code>gee_c2_s7</code>: SEEDS-GEE trained conditioning on 2 seeds for 7-day lead time.</li> <li><code>gpp_c2_s7_g3_r4</code>: SEEDS-GPP trained conditioning on 2 seeds for 7-day leadtime, where the label mixture is 3 GEFS members and 4 ERA reanalyses.</li> </ul>
Data from Uncertainty Ensembles of the MIT EPPA Model
<p>This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties. </p>
Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
<p>This respository corresponds to the following paper: Wankowicz et al. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM (2024). These are the qFit models. MTZ and deposited models cna be downloaded from the PDB. </p>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Development of Ensemble Steric and Electrostatic Chirality (ESEC) descriptors for modelling chromatographic enantioseparations
<p>For example for the Excel file with the title "Borate_chiral_explicit_biased_charged_uncharged.xlsx":</p> <p>- In the first tab, titled "Exp_water_acn_biased_charge", you can find the biased chiral descriptors for the molecules in their pH 9 state, simulated in explicit solvent. </p> <p>- In the second tab, titled "Exp_water_acn_biased_uncharged", you can find the biased chiral descriptors for the molecules in their uncharged state, simulated in explicit solvent. </p> <p>- The third tab, titled "Chiral log alfaRS charge uncharge", includes the responses (log αRS and αRS), along with the retention times and retention factors (k) for each molecule. In addition, this tab contains the descriptors from both the first and the second tab.</p> <p>- The fourth and the fifth tabs present the experimental and predicted responses (log αRS in the fourth tab and αRS in the fifth tab) for the models that were built. </p> <p>The structure of the other files follows a similar pattern: the first tabs provide the descriptor values, followed by a tab containing the modelling information (response(s) and various descriptor sets) and finally, a tab is included with the predicted response values. </p>
Ensembles of knowledge graph embedding models improve predictions for drug discovery
<p>This contains data described in detail in our paper, "Ensembles of knowledge graph embedding models improve predictions for drug discovery". The metadata involves the different trained models that were used for prediction analysis as well as all the predictions from the trained models.</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.