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99 results for “observational learning”
Learning Crisis Resource Management: Practicing Versus Observational Role in Simulation Training
ClinicalTrials.gov study NCT01653704. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Observation definitions and their implications in machine learning-based predictions of excessive rainfall
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On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data
<p>NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.</p>
Enhancing Retrievals of Air–Sea Heat Fluxes from AMSR2 Microwave Observations Based on Deep Learning
<p><span>The primary generated products are </span><span>2m</span><span> air temperature (Ta)</span><span> and</span><span> specific humidity (Qa), as well as the resulting fluxes of sensible heat (SHF)</span><span> and </span><span>latent heat (LHF). The data records for Ta</span><span> </span><span>and Qa are generated from </span><span>sea surface temperature (T</span><span>s</span><span>), column water vapour</span><span> (WV),</span><span> </span><span>wind speed (WS)</span><span>, column cloud liquid water</span><span> (CLW) </span><span>and rain rate</span><span> (RR)</span><span> data from JAXA’s Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1)</span><span> </span><span>spacecraft</span><span>.</span><span> The </span><span>model</span><span> for determining Ta, Qa, and U10 is the Matrices-Points Fusion Network (MPFNet). The resulting SHF</span><span> and</span><span> LHF fluxes are calculated from these fields of Ta, Qa, </span><span>WS</span><span>, and </span><span>Ts</span><span> using the Coupled Ocean-Atmosphere Response Experiment (COARE) 3.</span><span>6</span><span> flux algorithm</span><span>. </span><span>The </span><span>daily </span><span>MPFNet dataset is stored in </span><span>“nc”</span><span> format on 0.25°</span><span> </span><span><span> </span>0.25° gridded maps and span from July 2012 to December 2023.</span></p>
Joint Use of Far-Infrared and Mid-Infrared Observation for Sounding Retrievals: Learning from the Past for Upcoming Far-Infrared Missions
<p>Dataset and software (Matlab) used for the analysis of synoptic weather pattern, information content and retrieval estimates</p>
pywaterinfo dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This forcings dataset is the output of the pywaterinfo (https://fluves.github.io/pywaterinfo/) read in of forcing data (rain and potential evapotranspiration).</p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis</p>
OpenEO dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This dataset is the output of the <a href="https://openeo.org/">OpenEO</a> processing of satellite data (SAR backscatter and LAI). </p> <p>Code related to this dataset can be found <a href="https://github.com/olivierbonte/master_thesis">here</a></p>
Minimal dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>The minimal dataset needed of data which can not be retrieved from the internet by APIs in the preprocessing. Consists of shape, land use and rivers for the Zwalm catchment. </p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis </p>
Preprocessing output for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Outputs of the local preprocessing of the OpenEO data (see <a href="https://doi.org/10.5281/zenodo.7691342">here</a>) and pywaterinfo data (see <a href="https://doi.org/10.5281/zenodo.7689200">here</a>).</p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p>
Inverse observation operator parameters/models for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Both saved models and results of hyperparameter tuning are given. </p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p> <p> </p>
A Deep-Learning-Based Approach to the Downscaling of Precipitation Data Observed in Taiwan
<p>The data set is provided by the authors for the observational precipitation data downscaling method presented in the paper.</p>
Response of ecosystem productivity to high vapor pressure deficit and low soil moisture: lessons learned from the global eddy-covariance observations
<p>The generated datasets for conducting the analysis are available from the dataset of the FLUXNET2015 Tier one (https://fluxnet.org/data/download-data/), the AmeriFlux ONEFlux (https://ameriflux.lbl.gov/data/download-data/), and the ICOS Drought-2018 (https://www.icos-cp.eu/data-products/YVR0-4898). The FLUXNET2015 Tier one, the AmeriFlux, and the ICOS are all licensed under the Creative Commons Attribution 4.0 International license (CC-BY-4.0) (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p>
Code for the article titled "A New Approach to Ozone Profiling Based on MAX-DOAS Observations and Stacking Machine Learning Models"
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An Observational Study, Called FINEGUST, to Learn More About How People With Chronic Kidney Disease and Type 2 Diabetes Are Treated and How the Introduction of New Treatment Options, Like Finerenone,
ClinicalTrials.gov study NCT05526157. IPD Sharing: NO. Countries: 5. Publications: 1.
Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
ClinicalTrials.gov study NCT06032767. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
An Observational Study Called FIRST-2.0 to Learn More About the Use of the Study Treatment Finerenone Including How Safe it is and How Well it Works Under Real-world Conditions
ClinicalTrials.gov study NCT05703880. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of an Observer Tool on Learning Outcomes During High Fidelity Simulation
ClinicalTrials.gov study NCT03356717. IPD Sharing: NO. Countries: 1. Publications: 2.
Learning Curve of Ultrasound-Guided Suprainguinal Fascia Iliaca Block: A Prospective Observational Study
ClinicalTrials.gov study NCT06708598. IPD Sharing: NO. Countries: 1. Publications: 3.
LEARN-6™: A Prospective, Observational Nursing Home Study
ClinicalTrials.gov study NCT00727571. IPD Sharing: Not stated. Countries: 0. Publications: 2.
An Observational Study Called H2H-OSCAR-US to Learn More About How Well Rivaroxaban Works and How Safe it is Compared to Apixaban Under Real World Conditions in People in the US With Cancer Who Have P
ClinicalTrials.gov study NCT05461807. IPD Sharing: NO. Countries: 1. Publications: 1.
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)
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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.