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87 results for “model diagnostics”
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
LMDZOR-INCA global model simulations diagnostics for mineral dust direct radiative effet calculations
<p>This dataset contains the diagnostic variable used to estimate the mineral dust aerosol direct radiative effect from LMDZOR-INCA global simulations using different refractive index data and different size modes and a multimodal size distribution.</p> <p>The NetCDF files provide the radiation fields shortwave all sky (solswad, topswad) and clear sky (solswad0, topswad0) (sol is for the surface and top for the top of the atmosphere) and the longwave all sky (sollwad, toplwad) and clear sky (sollwad0, toplwad0), as monthly means over global grids.</p> <p>Data are provided for the mean, minimum and maximum of the complex refractive index from Di Biagio et al. (2017) ( https://doi.org/10.5194/acp-17-1901-2017 ) and the refractive index by Volz et al. (1973) ( <a href="https://doi.org/10.1364/AO.12.000564">https://doi.org/10.1364/AO.12.000564</a> ) in the longwave spectral range and for the refractive index by Balkanski et al. (2007) ( https://doi.org/10.5194/acp-7-81-2007) corresponding to 1.5% hematite by volume in the shortwave range.</p> <p>Simulations are performed for four lognormal size distributions with mass median diameters (sigma) of 1 µm (1.8), 2.5 µm (2), 7 µm (1.9), 22 µm (2). The multimodal run is performed on the size distribution obtained as the sum of the four modes combined follwing the mass fractions of 0.6%, 4.3%, 31.5%, and 63.6% for the four modes, respectively.</p> <p>Variables for the dust atmospheric load and optical depth at 550 nm for each mode are in the mean run for each mode.</p> <p>Input mass extinction efficiency (Ext, m2/g), absorption exitinction efficiency (Abs, m2/g), single scattering albedo (w) and asymmetry factor (g) for the different radiative bands at at some wavelengths used in the MODIS sensor are provided in the 1MODE_xxum_dust_optical_data_1.5dielectric_mixture.</p>
Data Archive: 2021 Development of a Virtual Diagnostic for the Advanced Particle Accelerator Modeling Code WarpX
<p><strong>A current promising field of research, laser-driven ion acceleration has the potential to reduce the size, cost, and energy consumption of particle accelerators by orders of magnitude.</strong></p> <p> </p> <p><strong>To better refine the instrumentation, we have developed a virtual diagnostic to measure electromagnetic radiation such as radiation produced from scattered and transmitted laser beams which has been implemented into WarpX, an advanced Particle-in-Cell code that simulates laser-driven particle acceleration. This “FieldProbe” diagnostic provides field measurements and is parallelized using the Message Passing Interface (MPI) and can thus run on High Performance Computing systems such as the NERSC Cori cluster.</strong></p>
Lanes, clusters, lines of Sight: Modelling diagnostic eyecare clinics to improve patient flow
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Datasets for the article "Evaporative controls on Antarctic precipitation: an ECHAM6 model study using innovative water tracer diagnostics"
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Model Data and Diagnostics used for the Age of Air Diagnostic Paper
<p>Model data and derived diagnostics used in the Age of Air paper, from Unified Model output. © Crown Copyright, Met Office</p>
Improved estimation of the prevalence of bovine cysticercosis and the diagnostic test characteristics in the absence of a reference standard using Bayesian Latent Class models, the example of Jimma and Ambo Abattoirs, Ethiopia
<p>Bovine cysticercosis is an infection of cattle musculature with the cestode parasite of humans known as Taenia saginata. This bovine cysticercosis data was collected from two Ambattoirs in Ethiopia namely Ambo and Jimma. Dissection of the predilection site, Ag-ELISA, and meat inspection were the diagnostic methods employed. Cysticerci collected during dissection of the predilection site were also confirmed using multiplex PCR. </p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication
ClinicalTrials.gov study NCT06633874. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Recommendations for population and individual diagnostic SNP selection in non-model species
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Predicting the impact of patient and private provider behaviour on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modelling approach
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Data from: External validation of an electronic health record-based diagnostic model for histological acute tubulointerstitial nephritis
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HRFMD (Hydrological model based Random Forest Model Diagnostics) results
<p>Results accompanying the publication titled: Advancing Hydrological Model Diagnostics: An Exploratory Approach Using Random Forest Models and Large-sample Catchment Dataset</p>
Model Data and Diagnostics used for the Lake Victoria Process Analysis
<p>Model data and derived diagnostics used in the Lake Victoria analysis, from Unified Model output. © Crown Copyright, Met Office</p>
Impact of a multi-disease integrated screening and diagnostic model for COVID-19, TB, and HIV in Lesotho during almost two years of pandemic
<p>These are pseudo-randomized data from the MISTRAL study: "Impact of a multi-disease integrated screening and diagnostic model for COVID-19, TB, and HIV in Lesotho during almost two years of pandemic". The data dictionary explains the data. Between December 2020 and August 2022, 4371 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho. </p>
Soil and atmospheric drought explain the biophysical conductance responses in diagnostic and prognostic evaporation models over two contrasting European forest sites
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript.</p>
Physician Reasoning on Diagnostic Cases With Large Language Models
ClinicalTrials.gov study NCT06157944. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnostic Reasoning With Customized GPT-4 Model
ClinicalTrials.gov study NCT06911645. IPD Sharing: NO. Countries: 1. Publications: 0.
Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
ClinicalTrials.gov study NCT05018715. IPD Sharing: NO. Countries: 1. Publications: 1.
Establish Diagnostic and Prognostic Models for Preclinical AD Patients Based on Multimodal MRI, Behavioral, Genetic, and Plasma Biomarkers
ClinicalTrials.gov study NCT06561906. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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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)
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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.