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218 results for “Physical Modelling”
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
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OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V10r (OCO3_L2_Standard) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-2 Level 2 geolocated XCO2 retrievals results, physical model Retrospective Processing V11.2r (OCO2_L2_Standard) at GES DISC
Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).
OCO-2 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V10r (OCO2_L2_Standard) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-2 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V11r (OCO2_L2_Standard) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
CMS: Simulated Physical-Biogeochemical Data, SABGOM Model, Gulf of America, 2005-2010
This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of America to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.
OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V11r (OCO3_L2_Standard) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
Boldine increases functional recovery and response to physical rehabilitation in a mouse model of contusion spinal cord injury
GEO Series GSE220907. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
IceQream: Quantitative chromosome accessibility analysis using physical TF models
GEO Series GSE305339. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
GDF15 neutralization restores muscle function and physical performance in a mouse model of cancer cachexia
GEO Series GSE214603. Mus musculus. 39 samples. Type: Expression profiling by high throughput sequencing.
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Evaluation of a mulit-physics snow model in the Tyrolean Alps
<p>This dataset presents an ensemble of 66240 simulations of the seasonal snow cover in the catchment of the Längentalbach (Tyrol, Austria) over the course of 5 winter seasons. Simulations are evaluated against point scale observations at the snow monitoring station Kühtai and catchment scale observations such as MODIS derived snow cover fraction as well as the seasonal water balance. Simulations are carried out with various parameter sets and forcing data error scenarios. The dataset splits in ModelSkills.csv where the resulting model performance values are listed, and ParameterAndForingError.csv where the corresponding parameter values and forcing errors are presented. The file header_info.txt provides additional information about each column in the files.</p>
A pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images
<p><strong>Introduction</strong></p> <p>This supporting information includes one figure S1, which is the representative elemental volume (REV) used in this study. And the figure S1 is consisted of 300 slices with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore.</p> <p> </p> <p>Figure S1 is uploaded with file name Figure S1-REV300.am, which is the representative elemental volume (REV) used in this study</p>
Pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images
<p>This supporting information includes one Figure-S1-REV300, which is the representative elemental volume (REV) used in this study. And the Figure-S1-REV300 is 300*300*300 with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore.</p> <p>Figure S1 is uploaded with file name Figure S1-REV300.tif, which is the representative elemental volume (REV) used in this study</p>
Aquaplanet experiment data for Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999
<p><strong>Aquaplanet experiment data from Webb and Lock (2020)</strong></p> <p><br> Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999</p> <p>CSV files containing data from Figs 1(b) and 2(a-d)</p> <p>Figure 2b:</p> <p>APEQ.Precipitation_mmperday.zonal.csv<br> APEQ_2LW_Cloud.Precipitation_mmperday.zonal.csv<br> APEQ_3LW_Cloud.Precipitation_mmperday.zonal.csv</p> <p>Figure 3a:</p> <p>APEQ.w700.zonal.csv<br> APEQ_3LW_Cloud.w700.zonal.csv<br> APEQ_2LW_Cloud.w700.zonal.csv</p> <p>Figure 3b:</p> <p>APEQ.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_2LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_3LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv</p> <p>Figure 3c:</p> <p>APEQ.Net_CRE_Wperm2.zonal.csv<br> APEQ_2LW_Cloud.Net_CRE_Wperm2.zonal.csv<br> APEQ_3LW_Cloud.Net_CRE_Wperm2.zonal.csv</p> <p>Figure 3d:</p> <p>APEQ4K-APEQ.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_2LW_Cloud-APEQ_2LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_3LW_Cloud-APEQ_3LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv</p> <p>Any queries please contact Mark Webb mark.webb@metoffice.gov.uk</p> <p> </p>
Supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" includes two animations showing a full Hudson Strait surge cycle with the default GSM heat flux.</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset contains the numerical model output files used in the analysis described in "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</p>
Climate model (CM2.6) and regional model (ACM) outputs used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key variables from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README.txt</em> for more details.</p>
Implementation of Inverse Model Policy based on KineNN in Physical Robot for Pick and Place Scenario
<p>Inverse Model Policy based on KineNN was trained in a simulation. After that, the policy is evaluated with the physical robot. The policy is used to drive the robot to specific target such as: Pick Position, Safe Pick Position, Place Position, Safe Place Position. </p>
Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</p> <p> </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.