365 datasets found

This dataset contains detailed inventories of 7 large plots of mangrove forests in the Utría National Park in the Colombian Pacific Coast. The inventory consists of individual...

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental...

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental...

This synthetic Siberian Larch tree crown dataset was created for upscaling and machine learning purposes as a part of the SiDroForest (Siberia Drone Forest Inventory) project....

The Qinghai-Tibet Plateau, known for its high altitude, cold climate, and fragile ecosystem, presents unique challenges and opportunities for the implementation of an...

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17...

Sentinel-3 OLCI images processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network...

IceCloudNet is a novel method based on machine learning able to obtain high quality vertically resolved predictions for ice water content and ice crystal number concentration of...

The experiment conducted aimed to enhance the temporal resolution of climate projections for agricultural applications by using machine learning to downscale daily...

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17...

Slides of the 19th OpenFOAM Workshop Training "Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim"

Slides: 18th OpenFOAM Workshop - Efficiently combining Machine Learning with OpenFOAM using SmartSim

<p>Generative artificial-intelligence (AI) models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling...

<p>The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations....

<div> <div> <div> <p>Predicting quasiparticle energies in materials requires expensive numerical evaluations of the electron self-energy. This limits...

This dataset contains the data necessary to recreate the counting procedure described in the manuscript "Combining artificial intelligence and local ecological knowledge to...

The ClimAVA_SWE data set — where ClimAVA stands for Climate Data for Adaptation and Vulnerability Assessments — provides high-resolution (4 km) future climate projections...

This project provides the dataset employed for the development of a machine learning framework designed to detect and interpret Tropical Cyclone Genesis (TCG) activity across...

This dataset provides high-resolution (30 m) spatialized near-surface air temperature products for the Qinghai-Tibet Plateau, updated using thermal infrared remote sensing data...

Satellite remote sensing enables global monitoring of water quality in freshwater and marine ecosystems. However, consistent data quality is a challenge due to variations in the...