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Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"

NIAID Data Ecosystem2026-05-01 收录

地球化学填图

不确定性量化

数据链接：

https://zenodo.org/record/10463742 数据链接链接失效反馈

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资源简介：

Data for "Uncertainty quantification in geochemical mapping: a review and recommendations".

应用场景：

创建时间：

2024-01-06

MonteCarlo模拟

不确定性量化

Compressed file containing a description of the methodology used to perform the Monte Carlo simulations and raw data from Figs 4 and 5. (ZIP)

Figshare2018-05-15 更新40

Data Supporting The Geochemical Atlas of Alaska, 2016

地球化学填图

元素分布建模

A rich legacy of geochemical data produced since the early 1960s covers the great expanse of Alaska, and there are significant opportunities for applying this information. To maximize the spatial dens

U.S. Geological Survey2016-01-01 更新10

Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning

脑循环模拟

不确定性量化

Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machin

Zenodo2022-05-25 更新00

MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"

概率预测评估

不确定性量化

This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select

NIAID Data Ecosystem20

Demographic description of the sample.

决策行为建模

不确定性量化

Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to mode

NIAID Data Ecosystem00

Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"

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