The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

Extracting data insights and generating visual data stories from tabular data are critical parts
of data analysis. However, most existing studies primarily focus on tabular data stored as flat …

Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto
programming environments. Many visualization researchers and practitioners have …

Profiling data by plotting distributions and analyzing summary statistics is a critical step
throughout data analysis. Currently, this process is manual and tedious since analysts must …

Recent publications suggest using natural language analysis on database schema
elements to guide tuning and profiling efforts. The underlying hypothesis is that state-of-the …

Abstract ydata-profiling is an open-source Python package for advanced exploratory data
analysis that enables users to generate data profiling reports in a simple, fast, and efficient …

It is widely accepted that data preparation is one of the most time-consuming steps of the
machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of …

Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address
this challenge by modeling and visualizing two kinds of auxiliary information:(1) quality–the …

This research employs federated learning using Convolutional Neural Networks (CNN)
across multi-institutional datasets to classify the severity of lung disease. The project …