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Dataset results
12 results for “eXplainable Artificial Intelligence”
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Using explainable artificial intelligence as a diagnostic tool for exploration of hydrologic connectivity at watershed scale
<p><strong><span>Using explainable artificial intelligence as a diagnostic tool for exploration of hydrologic connectivity at watershed scale</span></strong></p>
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend (Additional Data)
<p>Supplementary tables, additional data on the training datasets, and the testing data for yeast chronological assay for AgeXtend can be found here.</p>
Data from: Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians
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Repository for the codes and raw dataset used in the paper: Testing driving mechanisms of megathrust seismicity with Explainable Artificial Intelligence.
<p>This repository contains the codes/notebooks and raw dataset used in the paper:</p> <p>Testing driving mechanisms of megathrust seismicity with Explainable Artificial Intelligence. by Juan Carlos Graciosa, Fabio A. Capitanio, Adam Beall, Mitchell Hargreaves, Thyagarajulu Gollapalli, Titus Tang, Mohd Zuhair</p> <h3>xai-megathrust:</h3> <p>This directory contains the following:</p> <p>1. helper_pkg: Package containing helper routines used during the creation of grids.<br>2. in-data: Contains the processed but non-standardized features. Standardization is done during runtime.<br>3. ml4szeq: Main set of codes used in the study.<br>4. ntbk: Notebooks used in the study. This includes the sampling of the raw data into grids (0_grid_sampling.ipynb), creation of classification maps (1_make_classification_maps.ipynb), and the creation of LRP heatmaps (2_make_lrp_heatmaps.ipynb).<br>5. vis_pkg: Package used for creating maps</p> <p> </p> <h3>xai-megathrust-raw-data:</h3> <p>This contains the raw dataset. Here, the data prefix indicates the convergent region it is a part of and are as follows:</p> <p>1. alu: Alaska-Aleutians<br>2. cam: Central America<br>3. izu: Izu-Bonin-Mariana<br>4. ker: Tonga-Kermadec<br>5. kur: Japan-Kuriles-Kamchatka<br>6. ryu: Ryukyu-Nankai<br>7. sam: South America<br>8. sum: Southeast Asia </p> <p>This was adapted from the notation used by Hayes et al., 2018.</p>
Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
<p>This repository includes the input and output dataset, and python scripts used in the article, "Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)," of J. Chem. Phys. 156, 154108 (2022) [DOI: <a href="http://doi.org/10.1063/5.0087310">10.1063/5.0087310</a>] The repository also includes source data of figures in the article.</p>
Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology
ClinicalTrials.gov study NCT06617468. IPD Sharing: NO. Countries: 1. Publications: 0.
Microvascular Invasion Artificial Intelligence Prediction Via Contrast-enhanced Ultrasound With Explainability
ClinicalTrials.gov study NCT06760494. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Prediction and Management of Acute Kidney Injury With Explainable Artificial Intelligence
ClinicalTrials.gov study NCT05937451. IPD Sharing: NO. Countries: 1. Publications: 0.
An Explainable Neuroradiologist Artificial Intelligence Assistance System for Brain CT and MRI
ClinicalTrials.gov study NCT07167043. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe
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Data for "Exploring the Monthly Contribution of Drivers on European Summer Wildfires with Explainable Artificial Intelligence (XAI)"
<h3>Abstract</h3> <div> <p>We applied an XAI method to analyze the monthly contribution of wildfire drivers on summer fires in European forest, shrub and herbaceous vegetation areas from 2014 to 2023. Using burn area data and 18 features including meteorology, vegetation, topography, and anthropogenic activity, we developed a reliable wildfire occurrence model using the LSTM method.</p> </div> <h3>Methods</h3> <div> <p>The fire point data is provided by the European Forest Fire Information System (EFFIS). A total of 18 features were selected for modeling. Among these features, the four meteorological variables (Prep, LST, SM, and SR), along with the corresponding four condition indexes (RCI, TCI, SMCI, SRCI) derived from them, the Wind Speed (WS), and the two vegetation variables (Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI)), were used as monthly time series. The Lightning Frequency dataset is monthly but represents an average of data from 2012 to 2021, remaining constant across different years. The remaining six feature datasets do not vary over time. Data download and analysis were conducted using Google Earth Engine, QGIS, and Python.</p> </div> <h3>Subject keywords</h3> <p><span>Deep learning</span>, <span>Europe</span>, <span>Earth and related environmental sciences</span>, <span>vegetation</span>, <span>wildfire</span></p> <h3>Description of the data and file structure</h3> <ul> <li><strong><code>train_data_no_2023.npy</code></strong> and <strong><code>train_Y_data_no_2023.npy</code></strong>: These contain the features and labels for the training set (excluding data from 2023).</li> <li><strong><code>TEST_X_2023.npy</code></strong> and <strong><code>TEST_Y_2023.npy</code></strong>: These represent the features and labels for the test set, specifically for the year 2023.</li> <li><strong><code>model.h5</code></strong>: This is the final trained model.</li> <li><strong><code>shap_values_train_data.npy</code></strong>: This file contains the SHAP values for the training set, used to explain model predictions.</li> </ul> <p>The order of features:</p> <p>Label = ['Prep', 'LST', 'SM', 'SR', 'RCI', 'TCI', 'SMCI', 'SRCI', 'WS', 'NDVI', 'LAI', 'LF', 'CH', 'Elevation', 'Slope', 'Aspect', 'DR', 'DS']</p>
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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.
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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.