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Dataset results
43 results for “AI risk”
Dataset in support of an AI-aided chronic mixture risk assessment along a small European river
<p>Here, we make available a dataset to perform an AI-aided multi-scenario chronic mixture risk assessment. In 2021, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. The dataset of the chemical analysis was already published and can be found at DOI: 10.5281/zenodo.10892038. Furthermore, a detailed description of the dataset can be found at DOI: 10.1016/j.dib.2024.110510.</p> <p> </p>
AI Risk Ontology (AIRO)
Open the record for dataset details and reuse information.
Health AI Risk Taxonomy (HART)
Open the record for dataset details and reuse information.
Vocabulary of AI Risks (VAIR)
Open the record for dataset details and reuse information.
AI-Powered Fall Risk Prediction in Nursing Care
ClinicalTrials.gov study NCT07000981. IPD Sharing: NO. Countries: 1. Publications: 2.
Supplementary data: "Revealing drivers and risks for power grid frequency stability with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2106.04341">Revealing drivers and risks for power grid frequency stability with explainable AI</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/XAI-for-grid-frequency-stability">available at github</a>.</p> <p><strong>Data</strong></p> <p>For each area, the data folder contains the feature and target data used to train the ML model.</p> <ul> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>outputs.h5 </em>: The grid frequency stability indicators.</li> <li><em>version_2021-07-01</em>: Folder containing the training and test sets used for the results.</li> <li><em>documentation_of_data_download</em><strong>: </strong>Plots and information files concerning the ENTSO-E raw data and its aggregation.</li> </ul> <p><strong>Data sources</strong></p> <p>The data for input features (<em>raw_input_data.h5, input_forecast.h5 </em>and<em> input_actual.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on power grid frequency recordings from the German Transmission System Operator TransnetBW [2].</p> <p><strong>Results</strong></p> <p>For each area and each target, the result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The results refer to the full model and the restricted model (containing only day-ahead features).</p> <ul> <li><em>cv_results_gtb_full.csv</em> and <em>cv_results_gtb_day_ahead.csv</em> : Performance results for each combination in the hyper-parameter grid search.</li> <li><em>cv_best_params_gtb_full.csv</em> and <em>cv_best_params_gtb_day_ahead.csv</em> : Hyper-parameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> and <em>shap_interaction_values_gtb_full.npy </em>: First-order SHAP values and second-order SHAP interaction values for the full model.</li> <li><em>y_pred.h5</em> : Predictions of daily profile predictor, full model and day-ahead model.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p>
Artificial Intelligence (AI)-Assisted Risk-based Prostate Cancer Detection
ClinicalTrials.gov study NCT05443412. IPD Sharing: NO. Countries: 1. Publications: 24.
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer
ClinicalTrials.gov study NCT07354295. IPD Sharing: NO. Countries: 1. Publications: 1.
Artificial Intelligence-based Early Screening of Pancreatic Cancer and High Risk Tracing (ESPRIT-AI)
ClinicalTrials.gov study NCT04743479. IPD Sharing: NO. Countries: 1. Publications: 6.
AI-powered ECG Analysis Using Willem™ Software in High-risk Cardiac Patients (WILLEM)
ClinicalTrials.gov study NCT05890716. IPD Sharing: NO. Countries: 2. Publications: 4.
Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
ClinicalTrials.gov study NCT06206369. IPD Sharing: Not stated. Countries: 6. Publications: 1.
AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management
ClinicalTrials.gov study NCT06974188. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Risk of Asymptomatic Cerebral Embolism During AF Ablation With AI-HPSD Strategy Versus Standard Settings
ClinicalTrials.gov study NCT04408716. IPD Sharing: NO. Countries: 1. Publications: 9.
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Validation of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
ClinicalTrials.gov study NCT05787522. IPD Sharing: YES. Countries: 1. Publications: 0.
AI Risk Assessment Model for Complication Prevention in Plastic Surgery (Artificial Intelligence)
ClinicalTrials.gov study NCT06507384. IPD Sharing: YES. Countries: 1. Publications: 0.
Neoadj ph 2 AI Plus Everolimus in Postmenopausal Women w/ ER Pos/HER2 Neg, Low Risk Score
ClinicalTrials.gov study NCT02236572. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
ClinicalTrials.gov study NCT05009251. IPD Sharing: YES. Countries: 1. Publications: 0.
Validation of the TRAIN-AI for the Risk of HCC Recurrence After Liver Transplantation
ClinicalTrials.gov study NCT06799468. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Scoliosis-Specific Exercises for At-Risk AIS Curves
ClinicalTrials.gov study NCT02807545. IPD Sharing: NO. Countries: 1. Publications: 0.
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.