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Data Science in Science @DataSciScience

OA journal publishing original research and reviews at the intersection of #Science & #DataScience. Affiliated journal of the American Statistical Association.

Joined February 2021
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Data Science in Science@DataSciScience
7 Apr 2025
Great news! Data Science in Science has been accepted into Scopus! @tandfSTEM #Scopus #DataScience
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Predictions of Damages from Atlantic Tropical Cyclones in Two Scales: A Hierarchical Bayesian Study tandfonline.com/doi/full/10.…

Predictions of Damages from Atlantic Tropical Cyclones in Two Scales: A Hierarchical Bayesian Study

We propose joint distributional models of tropical cyclone characteristics and the economic damages they inflict, using a hierarchical Bayesian framework. This is done in two temporal scales: (i) a...

tandfonline.com
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Data Science in Science retweeted
Data Science in Science@DataSciScience
May 13
An Efficient, Accurate, and Interpretable Machine Learning Method for Computing Probability of Failure @tandfSTEM tandfonline.com/doi/full/10.…

An Efficient, Accurate, and Interpretable Machine Learning Method for Computing Probability of...

We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex syst...

tandfonline.com
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Data Science in Science retweeted
Data Science in Science@DataSciScience
7 Apr 2025
Great news! Data Science in Science has been accepted into Scopus! @tandfSTEM #Scopus #DataScience
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1,370
Data Science in Science retweeted
Paul Naish@PaulNaish78
May 15
Multi-Fidelity Bayesian Optimization with Gaussian Processes for Double Shell Inertial Confinement Fusion Target Design @DataSciScience @tandfSTEM tandfonline.com/doi/full/10.…

Multi-Fidelity Bayesian Optimization with Gaussian Processes for Double Shell Inertial Confinement...

Reliable, secure access to energy is a major focus for national security efforts. One potential route to such energy is through fusion reactions in inertial confinement fusion (ICF) experiments. Su...

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Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data tandfonline.com/doi/full/10.…
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Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor tandfonline.com/doi/full/10.…
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Data Science in Science retweeted
Data Science in Science@DataSciScience
Apr 2
Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks tandfonline.com/doi/full/10.…

Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks

Equation of state (EOS) tables are commonly used in hydrodynamic simulations of high-pressure, high-temperature phenomena in fields like planetary science, astrophysics, and high-energy-density sci...

tandfonline.com
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Data Science in Science retweeted
Paul Naish@PaulNaish78
Mar 20
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics tandfonline.com/doi/full/10.…

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmosph...

A large number of Deep Learning Weather Prediction (DLWP) architectures—based on various backbones, including U-Net, Transformer, Graph Neural Network, and Fourier Neural Operator (FNO)—have demons...

tandfonline.com
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Statistical Contrastive Learning for Spatio-Temporal Anomaly Detection @tandfSTEM tandfonline.com/doi/full/10.…

Statistical Contrastive Learning for Spatio-Temporal Anomaly Detection

Anomaly detection is an interdisciplinary research area which attracts substantial attention both in statistics and in machine learning due to its critical role in a wide range of diverse applicati...

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Data Science in Science retweeted
Data Science in Science@DataSciScience
6 Oct 2025
Dynamic Linear Models for Wastewater-Based Epidemiology with Missing Values: an Application to Covid-19 Surveillance @tandfSTEM #epidemiology #data #water #covid19 tandfonline.com/doi/full/10.…

Dynamic Linear Models for Wastewater-Based Epidemiology with Missing Values: an Application to...

We argue for the usage of dynamic linear models (DLMs) for pre-processing and modeling of wastewater data used for surveillance of population infectious disease levels. Unlike other existing models...

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Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source tandfonline.com/doi/full/10.…

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the...

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spal...

tandfonline.com
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Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations tandfonline.com/doi/full/10.…

Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium...

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced wa...

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Radioisotope Identification with List-Mode Gamma-Ray Data tandfonline.com/doi/full/10.…

Radioisotope Identification with List-Mode Gamma-Ray Data

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely...

tandfonline.com
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Modular Jump Gaussian Processes tandfonline.com/doi/full/10.…

Modular Jump Gaussian Processes

Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it ill-suited for mode...

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Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model tandfonline.com/doi/full/10.…

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model

Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These pro...

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Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data tandfonline.com/doi/full/10.…

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from...

As electric vehicle (EV) adoption continues to grow in the United States and worldwide, many have concerns that the grid may not be modernized fast enough to cope with the rapid electricity demand ...

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Are Neural Representation Learning Methods a Viable Alternative to TMLE for Causal Estimation? tandfonline.com/doi/full/10.…

Are Neural Representation Learning Methods a Viable Alternative to TMLE for Causal Estimation?

{Simulation is used to evaluate the performance of deep learning and semiparametric causal estimators under realistic high- and low-dimensional data-generating mechanisms from epidemiologic studies...

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Challenges with Dual Use of Auxiliary Variable: A Class of Estimators for Population Mean tandfonline.com/doi/full/10.…

Challenges with Dual Use of Auxiliary Variable: A Class of Estimators for Population Mean

The estimation of finite population mean is always of interest for different sampling techniques and it is the basic measure to find for sample to estimate one of the most applicable central tenden...

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