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Bulk chemistry measurements and XRF spectra of sediments from the high latitu...
This dataset has no description
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Quantified high-resolution TOC and CaCO3 contents of sediments from the high ...
This dataset has no description
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Bulk chemistry measurements of sediments from the high latitude sectors of Pa...
This dataset has no description
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Predicted Antarctic Heat Flow and Uncertainties using Machine Learning
We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets,... -
Training data for "Harnessing Machine Learning for Single-Shot Measurement of...
This repository contains data for the NeurIPS conference paper titled "". Raw data is provided in the following files:... -
Predicting electronic screening for fast Koopmans spectral functional calcula...
Koopmans spectral functionals represent a powerful extension of Kohn-Sham density-functional theory (DFT), enabling accurate predictions of spectral properties with... -
Predicting electronic screening for fast Koopmans spectral functional calcula...
Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art... -
Solar Wind Speed Prediction from Coronal Holes
The solar wind, a stream of charged particles originating from the Sun and transcending interplanetary space, poses risks to technology and astronauts. In particular geomagnetic... -
FINALES - Electrolyte optimization for maximum conductivity and for maximum c...
This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full... -
Replication Data for: Are nuclear masks all you need for improved out-of-doma...
This dataset is a processed version of the CAMELYON17 dataset used in the NeurIPS 2024 paper "Are nuclear masks all you need for improved out-of-domain generalization? A closer... -
University of Maryland classified LVIS georeferenced imagery of Arctic summer...
This data collection encompasses 1,387 classified LVIS georeferenced images, which include four classes: Ice, Melt Pond, Open Water, and Shadow. The original LVIS images were... -
Datasets for "Uncovering developmental time and tempo using deep learning"
This is the data repository for training and testing the Twin Network. The imaging data repositories are divided into several packages based on independent experiments. The data... -
Probing the effects of broken symmetries in machine learning
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to... -
The SWELL Knowledge Work Dataset for Stress and User Modeling Research
This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed... -
A Global Dataset for Seasonal to Annual Forecasts of GRACE-like Gridded Terre...
This dataset is linked to the manuscript titled "GRACE-FCast: a global long-lead forecast of total water storage for 2010-2024," which focuses on seasonal to annual forecasting... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical... -
A neural operator-based surrogate solver for free-form electromagnetic invers...
This dataset has no description
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A prediction rigidity formalism for low-cost uncertainties in trained neural ...
Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based...
