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136 results for “Model Versioning”
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 3 and the representative concentration pathway (RCP) used was RCP 7.0. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Extra-P Version Used for Noise-Resilient Empirical Performance Modeling with Deep Neural Networks
<p>This is the Extra-P source code that was used for the analysis and evaluation of the IPDPS 2021 paper "Noise-Resilient Empirical Performance Modeling with Deep Neural Networks". It also contains the checkpoints and saved models for the DNN part of the adaptive modeler as well as the gathered synthetic evaluation data.</p>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</p>
Code, Data, and Technical Note for SCREAM Beijing flood Convection-Permitting Regionally Refined Model 1.0 version
<p><a href="https://zenodo.org/api/records/15126670/draft/files/BeijingRRM-v0.1-SCREAM_push.tar.gz/content" target="_blank" rel="noopener noreferrer">BeijingRRM-v0.1-SCREAM_push.tar.gz</a> :</p> <p>The code used to generate all simulations for the paper entitled "Through the lens of a kilometer-scale climate model: 2023 Jing-Jin-Ji flood under climate change" submitted to Geophysical Research Letters. The SCREAM Beijing RRM source code is also available on GitHub at https://github.com/E3SM-Project/scream/tree/jzhang/RRM_tmp (last access: 28 Aug 2024) and a maint branch (BeijingRRM-v0.1; https://github.com/jsbamboo/scream/releases/tag/BeijingRRM-v0.1, last access: 28 Aug 2024). </p> <p><a href="https://zenodo.org/api/records/15126670/draft/files/files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz/content" target="_blank" rel="noopener noreferrer">files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz</a> : </p> <p>The runscripts, mapping files, masks used for analysis and figures in the paper. The simulation outputs and processed data are too large (3.3T) to upload to zenodo, and are available on the NERSC portal: https://portal.nersc.gov/archive/home/z/zhang73/www/files_scream-BeijingRRM-v1.0_storylines</p> <p><a href="https://zenodo.org/uploads/15126670" target="_blank" rel="noopener noreferrer">BeijingFlood_Doc.pdf</a> :</p> <p>The technical note documenting our practice in generating the SCREAM Beijing flood RRM configurations. Source page: https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/4056318237/SCREAM+Beijing+Flood+RRM+Technical+Note</p>
Antarctic surface climate and surface mass balance in the Community Earth System Model version 2 (1850-2100) - AWS data
<p>This Antarctica AWS temperature and wind speed dataset was compiled by Alexandra Gossart and Niels Souverijns (<a href="https://doi.org/10.1175/JCLI-D-19-0030.1">https://doi.org/10.1175/JCLI-D-19-0030.1</a>).</p>
English and Portuguese CBOW Models from Europarl Corpus, version 7, using FastText with Subwords Option
<p>The models were trained using FastText, model CBOW, 40 epochs, and subwords. Each *.BIN file has its *.VEC file with the vocabulary ordered by frequency. The *.BIN file can return a vector to represent an out-of-vocabulary (OOV) word if the necessary parts of the OOV word were used in training. FastText and Gensim can use these files. The English and Portuguese models are identified in the file name, <strong>_en_</strong> and <strong>_pt_</strong> respectively.</p> <p>An Excel file has the neighborhood changes of some selected words during training on each epoch. A previous exercise to find words with more than one meaning.</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts. CoastTrain-only version
<p><strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 and Landsat RGB images of coasts.</strong></p> <p><strong>Based on Coast Train*** data</strong></p> <p>These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: null, 1: water}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong>'_history.npz'</strong> files contain model training metrics</p> <p> </p> <p>One additional file, BEST_MODEL.txt, contains the name of the model with the highest validation accuracy</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p> <p>*** https://dbuscombe-usgs.github.io/CoastTrain/docs/Version%201:%20March%202022/data</p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts. SWED-only version
<p><strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.</strong></p> <p><strong>Based on SWED*** data</strong></p> <p>https://openmldata.ukho.gov.uk/</p> <p>These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: null, 1: water}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong>'_history.npz'</strong> files contain model training metrics</p> <p> </p> <p>One additional file, BEST_MODEL.txt, contains the name of the model with the highest validation accuracy</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p> <p>*** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584</p>
Ecore version of the meta-model for information dashboards (v3)
<p>The dashboard metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. This version includes the some modifications to better support the definition of dashboards.</p>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
Adaptive Caching for Operation-based Versioning of Models (Reproducibility Package)
Open the record for dataset details and reuse information.
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Evaluation data for the Intervention Model for Air Pollution (InMAP) version 1.6.1
<p>This directory contains data for performing InMAP model runs and evaluations. To the extent that any of the data is covered by third party licenses, it is the responsibility of the user to follow the terms of those licenses. A description of the contents of this directory is below:</p> <p>- annual_all_2005.csv: U.S. EPA annual average measured air pollution data for year 2005, downloaded from EPA AirData at http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html. </p> <p>- census2013blckgrp.shp and associated files: US Census Bureau estimates of average population counts for several demographic groups for years 2011-2015. Descriptive and license information regarding these files can be found in census2013blckgrp_README.txt</p> <p>- InMAPData_v1.6.1.gob: InMAP variable grid resolution input data for the continental U.S. year 2005 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command.</p> <p>- InMAPData_v1.2.0.ncf: Regular-grid InMAP input data for the continental U.S. year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from WRF-Chem simulation outputs with the 'inmap preproc' command. This file has not changed since version 1.2. Information regarding the WRF-Chem simulations is available in: Tessum, C. W., Hill, J. D., & Marshall, J. D. (2015). Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation. Geosci. Model Dev., 8(4), 957–973. http://doi.org/10.5194/gmd-8-957-2015</p> <p>- mortalityRates2013.shp and associated files: Year 2013 all-population, all-cause mortality rate data from the CDC WONDER database (http://wonder.cdc.gov). Descriptive information regarding these files can be found in mortalityRates2013_README.txt</p> <p>- states.shp and related files: U.S. state boundaries for making evaluation maps.</p> <p>- la_test directory: Files with the area surrounding the city of Los Angeles extracted for faster-running tests.</p> <p>- 2005_emissions directory: Anthropogenic emissions from the 2005 U.S. EPA National Emissions Inventory (NEI), processed using the AEP model (https://github.com/ctessum/aep) (files Elevated.shp and GroundLevel.shp), and biogenic and wilfire emissions (file bioFireEmis.shp) calculated following the methods in Tessum, C. W., Hill, J. D., & Marshall, J. D. (2015). Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation. Geosci. Model Dev., 8(4), 957–973. http://doi.org/10.5194/gmd-8-957-2015</p> <p>- singleSource directory: Regular-grid InMAP input data for los Angeles for year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from WRF-Chem simulation outputs with the 'inmap preproc' command, based on a 9 - 3 - 1 km nested WRF-Chem simulation which only included emissions from a single source in downtown Los Angeles. The directory includes four different InMAPData_*.ncf files, three corresponding to the three WRF-Chem domains and one for a nested InMAP simulation. The directory also contains an inmapEmis.shp shapefile and supporting files that contain emissions matching the emissions used in the WRF-Chem simulation. More information about this simulation can be found in the InMAP model description article.</p> <p>- FuelScenarios directory: This directory contains two subdirectories. "Emissions" contains spatial emissions information corresponding to the emissions scenarios described at doi: 10.1073/pnas.1406853111. "Concentrations" contains changes in pollutant concentrations resulting from WRF-Chem simulations of the emissions scenarios, also described and discussed at doi: 10.1073/pnas.1406853111.</p>
Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)"
<p>Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)". The dataset includes COS ecosystem, vegetation and soil fluxes, as well as CO2 exchange fluxes. COS soil fluxes are calculated through the Ogee et al. (2016) soil model, and COS mole fractions vary spatially and temporally. The site-specific files provide SiB4 output only for the plant functional type that is represented by this site. The plant functional type is included in the file name. Datasets include 3 hourly output between 2000-2020 for site simulations, and monthly output between 2000-2020 for global simulations (at 0.5x0.5 degrees). The SiB4 model code is available online: https://gitlab.com/kdhaynes/sib4_corral</p>
Roach et al. (2019) coupled wave-ice model output (hourly coupling version): Beaufort Sea 2012-2019
<p>Wavewatch III model output from Roach et al. (2019) coupled wave-ice model with hourly coupling from the central Beaufort Sea, spanning 2012-2019.</p> <p>See manuscript below for further details:</p> <p>Roach, L., C. Bitz, C. Horvat, and S. Dean (2019), Advances in modelling interactions between sea ice and ocean surface waves. Journal of Advances in Modeling Earth Systems</p>
daleihao/Topographic_Effects: Codes and data for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"
<p>Codes and data to reproduce all results and plot all figures for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"</p>
Nemo2 (Numbers, fEatures, MOdels, version 2) Testing Files
<p>The publications and research associated with this software is currently under review in the "<em>Journal</em> of <em>Systems</em> and <em>Software</em>".</p> <p>You can find the official Github repository of this dataset in: <a href="https://github.com/danieljmg/Nemo2_models">https://github.com/danieljmg/Nemo2_models</a></p>
ScienceDex guides
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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.