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5,805 results for “Data model”
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Data driven modeling of the simultaneous activities in ambient environments
Resident of a smart home, who may be an old person or an Alzheimer patient needing permanent assistance, actuates the world by realizing activities, which are observed through the embedded sensors of smart home. Typically, this person may sometimes forget completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In order to provide automatic assistance for the smart home resident through the embedded electronically controllable actuators and make the smart home resident able to live independently at home we propose to calculate a possibilistic logical space for correct realization of activities, which may be represented in form of a multivariable problem. Regardless from the physical entity (modality and location) of the intelligence source and the quantity of individuals who perform the activities; per each possible goal or activity, we consider a unique source of intelligence (for example a social mind) who directs the order of fuzzy events that occur in the ambient environment, then the plan behind world actuations is modeled applying extensions of the fuzzy logic. The main key point that we deal with is the analysis of the observations in order to make inferences about possible simultaneous activities that may be planned and realized by one or more individuals; so that we can reason in the cases the parallel activities are interrupted.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Aircraft Proximity Maps Based on Data-Driven Flow Modeling
With the forecast increase in air traffic demand over the next decades, it is imperative to develop tools to provide traffic flow managers with the information required to support decision making. In particular, decision-support tools for traffic flow management should aid in limiting controller workload and complexity, while supporting increases in air traffic throughput. While many decision-support tools exist for short-term traffic planning, few have addressed the strategic needs for medium- and long-term planning for time horizons greater than 30 minutes. This paper seeks to address this gap through the introduction of 3D aircraft proximity maps that evaluate the future probability of presence of at least one or two aircraft at any given point of the airspace. Three types of proximity maps are presented: presence maps that indicate the local density of traffic; conflict maps that determine locations and probabilities of potential conflicts; and outliers maps that evaluate the probability of conflict due to aircraft not belonging to dominant traffic patterns. These maps provide traffic flow managers with information relating to the complexity and difficulty of managing an airspace. The intended purpose of the maps is to anticipate how aircraft flows will interact, and how outliers impact the dominant traffic flow for a given time period. This formulation is able to predict which "critical" regions may be subject to conflicts between aircraft, thereby requiring careful monitoring. These probabilities are computed using a generative aircraft flow model. Time-varying flow characteristics, such as geometrical configuration, speed, and probability density function of aircraft spatial distribution within the flow, are determined from archived Enhanced Traffic Management System data, using a tailored clustering algorithm. Aircraft not belonging to flows are identified as outliers.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
MAVEN EUV Modelled Data Bundle
This bundle contains solar irradiance spectra in 1-nm bins from 0-190 nm. The spectra are generated based upon the Flare Irradiance Spectra Model - Mars (FISM-M) using the EUV calibrated band irrandiance and interpolated Earth-based solar indices and measurements as proxies. The data were provided by the MAVEN EUV team in CDF format.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: CFD Validation of Synthetic Jets and Turbulent Separation Control
CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Code for a data-driven model
<p>The archived code is to illustrate the framework of the data-driven model proposed in the article "A machine-learning-based model for water quality in coastal waters, taking dissolved oxygen and hypoxia in Chesapeake Bay as an example" submitted to Water Resource Research. </p> <p>A sample run is added. </p> <p>Additional information can be found in the "ReadMe" file within the archived zip file. </p>
Data for paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda
<p>Dataset used by code for the paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in RwandaFine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda</p>
Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.
<p>Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>A geometrical understanding of the individual patient's disease morphology is crucial in aortic surgery. The aim of our study was to validate a questionnaire addressing understanding of aortic disease and use this questionnaire to investigate the value of 3D printing as a teaching tool for surgical trainees.</p> <p><strong>Methods: </strong>Anonymized CT-angiography images of six different patients were selected as didactic cases of aortic disease and made into 3D models of transparent rigid resin with the Vat-photopolymerization technique. The 3D aortic models, which could be disassembled and reassembled, were displayed to 37 surgical trainees, immediately after a seminar on aortic disease. A questionnaire was developed to compare the trainees' understanding before (T0) and after (T1) demonstration of the 3D printed models.</p> <p><strong>Results: </strong>A panel of 15 experts participated in evaluating face and content validity of the questionnaire. The questionnaire validity was established and therefore the information investigated by the questionnaire could be synthetized using the mean of the items to indicate the understanding. The participants (mean age 28 years, range 26-34, male 59%) showed a significant improvement in understanding from T0 (median=7.25; IQR=1.50) to T1 (median=8.00; IQR=1.50; P=0.002).</p> <p><strong>Conclusions: </strong>Preliminary data suggest that the use of 3D-printed aortic models as a teaching tool was feasible and improved the understanding of aortic disease among surgical trainees.</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.