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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.

 New York
tandfonline.com/udss
Joined February 2021
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Data Science in Science@DataSciScienceJun 29
Predicting Atomistic Transitions with Transformers @tandfSTEM #MachineLearning #Data #Materials tandfonline.com/doi/full/10.…
Predicting Atomistic Transitions with Transformers

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find th...

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Paul Naish@PaulNaish78Sep 4
Election Day 2024: Exit Polling as a High-Impact Data Science Project to Engage Students in Civic Responsibility @tandfonline tandfonline.com/doi/full/10.…
Election Day 2024: Exit Polling as a High-Impact Data Science Project to Engage Students in Civic...

Election Day 2024: Exit Polling as a High-Impact Data Science Project to Engage Students in Civic...

This article shares a model for conducting a student-led exit polling project connected to American elections. The author, in his fall 2024 introductory data science course, enlisted his students t...

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Data Science in Science@DataSciScienceJun 22
Parameter Estimation and Seasonal Modification of the Fractional Poisson Process with Application to Vorticity Extremes over the North Atlantic tandfonline.com/doi/full/10.…
Parameter Estimation and Seasonal Modification of the Fractional Poisson Process with Application...

Parameter Estimation and Seasonal Modification of the Fractional Poisson Process with Application...

The fractional Poisson process (FPP) generalizes the standard Poisson process by replacing exponentially distributed return times with Mittag-Leffler distributed ones with an extra tail parameter, ...

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Paul Naish@PaulNaish78Jul 17
Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation @DataSciScience tandfonline.com/doi/full/10.…
Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of resear...

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Data Science in Science@DataSciScienceJun 19
Bayesian Deep Gaussian Processes for Correlated Functional Data: a Case Study in Cosmological Matter Power Spectra tandfonline.com/doi/full/10.…
Bayesian Deep Gaussian Processes for Correlated Functional Data: a Case Study in Cosmological...

Bayesian Deep Gaussian Processes for Correlated Functional Data: a Case Study in Cosmological...

Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and...

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Data Science in Science@DataSciScienceJun 9
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

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...

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Extreme temperatures on the Greenland ice sheet: a spatial model @tandfSTEM @tandfenviro #Greenland #SDG #Ice #ClimateChange #Statistics tandfonline.com/doi/full/10.…

Extreme temperatures on the Greenland ice sheet: a spatial model

Whilst surface ice melt from the Greenland ice sheet is one of the largest contributors to sea level rise, our understanding of this phenomenon arises mostly from case studies of specific melt even...

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Paul Naish@PaulNaish78Jun 16
A Probabilistic Framework for Bias Correction and Fusion of Multi-Model Datasets @DataSciScience @tandfSTEM tandfonline.com/doi/full/10.…
A Probabilistic Framework for Bias Correction and Fusion of Multi-Model Datasets

A Probabilistic Framework for Bias Correction and Fusion of Multi-Model Datasets

Reliable outputs from physics-based models are essential for a wide range of scientific and operational applications, such as energy systems. However, physics-based numerical models often produce s...

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Data Science in Science@DataSciScienceMay 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...

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...

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Data Science in Science@DataSciScience7 Apr 2025
Great news! Data Science in Science has been accepted into Scopus! @tandfSTEM #Scopus #DataScience
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Paul Naish@PaulNaish78May 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...

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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Data Science in Science@DataSciScienceApr 2
Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks tandfonline.com/doi/full/10.…
Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks

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...

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Paul Naish@PaulNaish78Mar 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...

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...

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Data Science in Science@DataSciScience6 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...

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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Paul Naish@PaulNaish7830 Sep 2025
New Journal alert - ASA Discoveries! New OA journal from the American Statistical Association and published by @tandfonline @tandfSTEM Check out the opening Editorial below! ⬇️⬇️⬇️⬇️⬇️⬇️⬇️ tandfonline.com/doi/full/10.…

Editorial

Published in ASA Discoveries (Vol. 1, No. 1, 2025)

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Data Science in Science@DataSciScience16 Apr 2025
THANOS: A Predictive Model of Electoral Campaigns Using Twitter Data and Opinion Polls tandfonline.com/doi/full/10.…
THANOS: A Predictive Model of Electoral Campaigns Using Twitter Data and Opinion Polls

THANOS: A Predictive Model of Electoral Campaigns Using Twitter Data and Opinion Polls

The influence and impact of social media campaigns on democratic elections is a critical area of research in modern big-data analytics. While the efficacy of using social media data for forecasting...

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Paul Naish@PaulNaish784 Apr 2025
Free Webinar - sign up now! Apr 22, 2025 - "Escape the Crop Circle Conundrum" @maanow @tandfSTEM #Scatterplot #mathematics #DataScience bit.ly/43DyyzJ

Escape the Crop Circle Conundrum

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Statistics@Statistics_Jour26 Nov 2024
New Article Collection (free to read!): 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 tandfonline.com/journals/gst…
Machine learning and Statistical learning

Machine learning and Statistical learning

Explore the article collection: Machine learning and Statistical learning. Published in Statistics.

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Data Science in Science@DataSciScience5 Nov 2024
Time-Varying ℓ0 Optimization for Spike Inference from Multi-Trial Calcium Recordings tandfonline.com/doi/full/10.…
Time-Varying ℓ0 Optimization for Spike Inference from Multi-Trial Calcium Recordings

Time-Varying ℓ0 Optimization for Spike Inference from Multi-Trial Calcium Recordings

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving anima...

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Paul Naish@PaulNaish7811 Nov 2024
𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗖𝗮𝗻𝗰𝗲𝗿 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 #Statistics #imaging #data #Cancer @tandfSTEM @AmstatNews tandfonline.com/doi/full/10.…
Statistical Analysis of Quantitative Cancer Imaging Data

Statistical Analysis of Quantitative Cancer Imaging Data

Recent advances in types and extant of medical imaging technologies has led to proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data refer to numerical ...

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