Data Science in Science

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

[Data Science in Science @DataSciScience]

Great news! Data Science in Science has been accepted into Scopus!

@tandfSTEM #Scopus #DataScience

[Data Science in Science @DataSciScience]

Great news! Data Science in Science has been accepted into Scopus!

@tandfSTEM #Scopus #DataScience

[Paul Naish @PaulNaish78]

Multi-Fidelity Bayesian Optimization with Gaussian Processes for Double Shell Inertial Confinement Fusion Target Design

@DataSciScience @tandfSTEM https://t.co/9X2GB5un6S

[Data Science in Science @DataSciScience]

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

@tandfSTEM https://t.co/0BMWqBz0kH

[Data Science in Science @DataSciScience]

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data https://t.co/ylO4tgXcvx

[Data Science in Science @DataSciScience]

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor https://t.co/tmXQhsJuVl

[Data Science in Science @DataSciScience]

Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks https://t.co/wYhz7Srn0m

[Data Science in Science @DataSciScience]

Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks https://t.co/wYhz7Srn0m

[Paul Naish @PaulNaish78]

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics https://t.co/skSKqXxq2K

[Data Science in Science @DataSciScience]

Statistical Contrastive Learning for Spatio-Temporal Anomaly Detection

@tandfSTEM https://t.co/W3Tt5zttnj

[Data Science in Science @DataSciScience]

Dynamic Linear Models for Wastewater-Based Epidemiology with Missing Values: an Application to Covid-19 Surveillance

@tandfSTEM #epidemiology #data #water #covid19 https://t.co/3Vk6LqHUud

[Data Science in Science @DataSciScience]

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source https://t.co/66JCvKHTxw

[Data Science in Science @DataSciScience]

Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations https://t.co/RDLg6XDDWq

[Data Science in Science @DataSciScience]

Radioisotope Identification with List-Mode Gamma-Ray Data https://t.co/nCZNVMg2ek

[Data Science in Science @DataSciScience]

Modular Jump Gaussian Processes https://t.co/jGeVZCUsaQ

[Data Science in Science @DataSciScience]

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model https://t.co/5IsbeSZYD1

[Data Science in Science @DataSciScience]

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data https://t.co/aJmHLzWkHO

[Data Science in Science @DataSciScience]

Are Neural Representation Learning Methods a Viable Alternative to TMLE for Causal Estimation? https://t.co/RqwUkeBV8L

[Data Science in Science @DataSciScience]

Challenges with Dual Use of Auxiliary Variable: A Class of Estimators for Population Mean https://t.co/Kr9OwhD8Y6

[Data Science in Science @DataSciScience]

Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study https://t.co/HJwlaKJzQL

[Data Science in Science @DataSciScience]

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments

@tandfSTEM https://t.co/T3vvU36TjQ