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99
datasets available to search
ShareScore release 0.9.0
Dataset results
99 results for “observational learning”
An Observational Study Called STAR-T to Learn More About the Sequential Treatment With Regorafenib and TAS-102 in Adults With Metastatic Colorectal Cancer Under Real World Conditions
ClinicalTrials.gov study NCT05839951. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study to Learn More About How Well a Treatment Works When Given After Treatment With Atezolizumab and Bevacizumab or Another Similar Combination of Drugs in Adults With Liver Cancer T
ClinicalTrials.gov study NCT06117891. IPD Sharing: NO. Countries: 15. Publications: 0.
An Observational Study to Learn More How Chronic Kidney Disease Gradually Changes Over Time in Adults Using Electronic Healthcare Records (CKD Natural History Study)
ClinicalTrials.gov study NCT05914259. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study to Learn More About the Real-world Outcomes in Patients With Heart Failure Who Initiate Treatment With Vericiguat in Japan
ClinicalTrials.gov study NCT06697353. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study Called FIRST-2.0 China 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 in a Chinese Popu
ClinicalTrials.gov study NCT07124039. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study (Called RETAF-PS) Using a Patient Survey to Learn More About Treatment Outcomes in Patients With Irregular and Often Rapid Heartbeat (Atrial Fibrillation) Treated With Apixaban
ClinicalTrials.gov study NCT05471830. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study Called ROVER to Learn More About How Well Vericiguat Works in People Who Were Newly Treated With Vericiguat in Routine Medical Care in Germany
ClinicalTrials.gov study NCT06486844. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study to Learn More About the Use of Androgen Receptor Inhibitors and How They Affect Men With Nonmetastatic Prostate Cancer in Routine Medical Care in the United States
ClinicalTrials.gov study NCT06204302. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study to Learn About the Occurrence of Disseminated Intravascular Coagulation Among Adults With Sepsis in Japan
ClinicalTrials.gov study NCT06373159. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study in the United States to Learn How Venous Thromboembolism, Disseminated Intravascular Coagulation, and Sepsis Are Related
ClinicalTrials.gov study NCT06765681. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study to Learn More About Vericiguat Treatment Patterns and Its Safety in People With Chronic Heart Failure With Reduced Ejection Fraction in Routine Medical Care in the United States
ClinicalTrials.gov study NCT06363110. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study Called FLAMINgO to Learn More About the Treatment Combination of Finerenone and SGLT2 Inhibitors in People With Long-term Kidney Disease (Chronic Kidney Disease) Together With T
ClinicalTrials.gov study NCT05640180. IPD Sharing: NO. Countries: 1. Publications: 0.
A Pharmacy-based Observational Study to Learn More About Iberogast Advance in the Real-world Setting
ClinicalTrials.gov study NCT05389709. IPD Sharing: NO. Countries: 1. Publications: 0.
Synthetic basement depth, gravity anomalies, density and observation points training dataset to train deep learning model
<p>Contains 200000 training data for our DNN model.</p>
An Observational Study to Learn More About How Well Damoctocog Alfa Pegol Works in Previously Treated Children With Hemophilia A
ClinicalTrials.gov study NCT07088458. IPD Sharing: NO. Countries: 0. Publications: 0.
Motor Imagery and Action Observation on Motor Learning
ClinicalTrials.gov study NCT04191083. IPD Sharing: NO. Countries: 1. Publications: 0.
Learning to Improve Earth Observation Flight Planning
This paper describes a method and system for integrating machine learning with planning and data visualization for the management of mobile sensors for Earth science investigations. Data mining identifies discrepancies between previous observations and predictions made by Earth science models. Locations of these discrepancies become interesting targets for future observations. Such targets become goals used by a flight planner to generate the observation activities. The cycle of observation, data analysis and planning is repeated continuously throughout a multi-week Earth science investigation.
Machine Learning for Earth Observation Flight Planning Optimization
This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.
Datasets for "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning"
<p>Go to https://zenodo.org/doi/10.5281/zenodo.8388482 for the updated version!!</p> <p> </p> <p>These datasets are the official data of the submission "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning", anonymous for the double-blind submission system.<br>A README.md file will contain all the information related to the datasets and a metadata.json file will contain their metadata according to the Schema.org format.</p>
ScienceDex guides
Understand access before you commit
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.