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Dataset results
22 results for “Loop Modeling”
Human-AI Collaboration: A tool to enable AI model generation with human-in-the-loop
<p>Human-AI collaboration enables domain experts to contribute their expertise with the goal of enhancing the knowledge learned by the AI models from the patterns in the data. This enables the integration of domain-specific knowledge to enrich the data for further improvement of the models through retraining. The human-AI collaboration is composed of multiple sub-components and interfaces that enables communication with external systems such as data sources, model repositories, machine configurations and decision support systems.</p> <p>Human-AI Collaboration component is developed using Python programming language. The frontend is developed using Streamlit1. The backend is developed using python and the API is implemented using FastAPI2. The choice of the programming language was made because of its wide usage and vast user base. The frameworks Streamlit and FastAPI are chosen because of the rich features for functionality and documentation as well as suitability for data analysis tasks. The applications are packaged as docker images for deployment. The application runs as a web application served by nginx for reverseproxying and users can access it via client applications such as web browsers or REST clients like Postman.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Data for "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling"
<p>We provide the data and scripts used to produce the figures shown in our publication "Entanglement Dynamics in Monitored Kitaev Circuits:<br>Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling".</p>
Data analysis results for: "MoDLE: High-performance stochastic modeling of DNA loop extrusion interactions"
<p>Due to technical issues we are unable to upload the updated version of this dataset on Zenodo.<br> <br> The latest version of this dataset can be found on the NRID research data archive at DOI <a href="https://doi.org/10.11582/2022.00056">10.11582/2022.00056</a>.</p>
Data from simulations of the thalamocortical loop model
<p>This zip file contains, separated into different folders, the data and metadata resulting from simulating different stimuli protocols with a thalamocortical spiking network model (https://github.com/dguarino/T2). The model was developed using the Mozaik framework (https://github.com/antolikjan/mozaik), which itself relies on PyNN (http://neuralensemble.org/PyNN/), NEST (https://www.nest-simulator.org/), and other libraries (see the Mozaik specs).</p> <p>Inside the zipped folder, there will be the following sub-folders, each containing the python pickled output recording of the recorded spikes (with Vm, and conductances for a subset of simulated neurons) in the Neo format (https://neo.readthedocs.io/en/stable/ ):</p> <p>ThalamoCorticalModel_data_contrast_closed_____<br> ThalamoCorticalModel_data_contrast_open_____<br> ThalamoCorticalModel_data_luminance_closed_____<br> ThalamoCorticalModel_data_luminance_open_____<br> ThalamoCorticalModel_data_orientation_closed_____<br> ThalamoCorticalModel_data_orientation_feedforward_____<br> ThalamoCorticalModel_data_orientation_open_____<br> ThalamoCorticalModel_data_size_closed_____<br> ThalamoCorticalModel_data_size_closed_____large<br> ThalamoCorticalModel_data_size_closed_cross-oriented_____<br> ThalamoCorticalModel_data_size_feedforward_____<br> ThalamoCorticalModel_data_size_feedforward_____large<br> ThalamoCorticalModel_data_size_feedforward_____old<br> ThalamoCorticalModel_data_size_LGNonly_____<br> ThalamoCorticalModel_data_size_nonoverlapping_____<br> ThalamoCorticalModel_data_size_open_____<br> ThalamoCorticalModel_data_size_overlapping_____<br> ThalamoCorticalModel_data_size_overlapping_____old<br> ThalamoCorticalModel_data_spatial_closed_____<br> ThalamoCorticalModel_data_spatial_Kimura_____<br> ThalamoCorticalModel_data_spatial_LGNonly_____<br> ThalamoCorticalModel_data_spatial_open_____</p>
Data for: How do we measure and increase systems thinking? Comparing self-reported and performative metrics in response to building causal loop models
Open the record for dataset details and reuse information.
Dataset supporting the manuscript "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, Kasal-Hoc et al, Veterinary Sciences, 2021)
<p>These are the data supporting reported results in the publication "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, A, Kasal-Hoc N. et al, Veterinary Sciences, 2021). DOI not yet available.</p>
Rosetta Loop Modeling Data for "A Systematic Approach for Evaluating the Role of Surface-Exposed Loops in Trypsin-like Serine Proteases: Analysis of the 170 loop in Coagulation Factor VIIa"
<p>Rosetta Loop Modeling data for the publication "A Systematic Approach for Evaluating the Role of Surface-Exposed Loops in Trypsin-like Serine Proteases: Analysis of the 170 loop in Coagulation Factor VIIa." See the included readme.txt for more details. Please cite the paper if you use these data.</p>
An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation (dataset)
<p>5 subjects (4 PD and 1 ET) IMU and sEMG raw data supporting the conclusions of the article "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation"</p> <p>name standard:<br>pacientnumber_age_sex_disease_arm_timesincediagnose.mat</p> <p>Xs, Xs1 ... Xs17 are the time vectors for sEMG and IMU. </p> <p> </p> <p><strong><em>If you use any part of this data for your research, please cite our paper:</em></strong></p> <pre>@article{pinheiro2024opensim, title={An OpenSim-based closed-loop biomechanical wrist model for subject-specific pathological tremor simulation}, author={Pinheiro, Wellington C and Ferraz, Henrique B and Castro, Maria Claudia F and Menegaldo, Luciano L}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, year={2024}, publisher={IEEE} }</pre> <p> </p> <p>Contact: wellington@peb.ufrj.br</p>
Transformer Models for Disconnection-Aware Triple Transformer Loop
<p>Models of the Triple Transformer Loop for retrosynthesis trained using OpenNMT.</p> <p>Full details in <a href="https://doi.org/10.1039/d3sc01604h">Chemical Science</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <div> <div> <div class="highlighter--icon highlighter--icon-copy"> </div> <div class="highlighter--icon highlighter--icon-change-color"> </div> <div class="highlighter--icon highlighter--icon-delete"> </div> </div> </div>
Construct Validity of a Large Loop Excision of the Transformation Zone (LLETZ) Training Model
ClinicalTrials.gov study NCT02476500. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of the Accuracy, Safety and Robustness of a Single-input-single-output (SISO) Model-based Predictive Closed-loop System to Guide Patient-individualized ICU Sedation
ClinicalTrials.gov study NCT00735631. IPD Sharing: Not stated. Countries: 1. Publications: 1.
NOMOTHETICOS: Nonlinear Modelling of Thyroid Hormones' Effect on Thyrotropin Incretion in Confirmed Open-loop Situation
ClinicalTrials.gov study NCT01145040. IPD Sharing: YES. Countries: 1. Publications: 17.
Assessment of a New Closed-loop Algorithm in Type 1 Diabetes (Saddle Point Model Predictive Control : SP-MPC) (PPA)
ClinicalTrials.gov study NCT02061488. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MMS SITL Ground Loop: Data for the GLS-MP Magnetopause Model
<p>Data required to run the mp-dl-unh model used for magnetopause classification by NASA's Magnetospheric Multiscale (MMS) mission. Included are the model weights, the scikit-learn scaling parameters, and the training and validation dataset used when creating the model. The model itself is available through GitHub and best run on Google Colabs.</p> <p><strong>Releases associated with the data:</strong></p> <p><a href="https://doi.org/10.5281/zenodo.3891992">GLS-MP</a>: Notebooks that used the data in this deposit to train, validate, and run the unh-mp-dl model</p> <p><a href="http://github.com/colinrsmall/mp-dl-unh">mp-dl-unh</a>: Software used to run the model at the MMS SDC</p> <p><a href="https://doi.org/10.5281/zenodo.3891944">MMS SITL Ground Loop</a>: Notebooks used to create tables and figures in the published paper</p> <p><a href="https://doi.org/10.5281/zenodo.3894873">PyMMS</a>: Softwares used to download MMS data and burst selections</p>
Genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model
GEO Series GSE225087. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Development of an in vivo ligated loop model reveals new insight into the host immune response against Campylobacter jejuni
GEO Series GSE147629. Campylobacter jejuni. 78 samples. Type: Expression profiling by high throughput sequencing.
The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies
Open the record for dataset details and reuse information.
A Study to Evaluate a Multiple Model Probabilistic Predictive Controller (MMPPC) for Closed Loop Insulin Delivery
ClinicalTrials.gov study NCT01492062. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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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.