Multiomics technologies with single-cell and spatial resolution make it possible to measure
thousands of features across millions of cells. However, visual analysis of high-dimensional …

The trouble with data is that it frequently provides only an imperfect representation of a
phenomenon of interest. Experts who are familiar with their datasets will often make implicit …

Visual data analysis tools provide people with the agency and flexibility to explore data
using a variety of interactive functionalities. However, this flexibility may introduce potential …

Latent vectors extracted by machine learning (ML) are widely used in data exploration (eg, t-
SNE) but suffer from a lack of interpretability. While previous studies employed disentangled …

Plots and tables are commonplace in today's data-driven world, and much research has
been done on how to make these figures easy to read and understand. Often times …

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still
requires human intervention to be functional. However, the complex and collaborative …

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing,
and interpreting data. This cyclical way of working creates challenges within the linear …

We propose TimeTables, a novel prototype system that aims to support data exploration,
using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space …

Researchers collect large amounts of user interaction data with the goal of map** user's
workflows and behaviors to their high‐level motivations, intuitions, and goals. Although the …

Data provenance is being one of the emerging needs for the domains and technologies to
grant end-user to analyse and evaluate data life-cycle. Particularly in Big Data world with the …