Exploratory data analysis plays a major role in obtaining insights from data. Over the last two
decades, researchers have proposed several visual data exploration tools that can assist …

Working with data in table form is usually considered a preparatory and tedious step in the
sensemaking pipeline; a way of getting the data ready for more sophisticated visualization …

Data science is characterized by evolution: since data science is exploratory, results evolve
from moment to moment; since it can be collaborative, results evolve as the work changes …

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

Many long-established, traditional manufacturing businesses are becoming more digital and
data-driven to improve their production. These companies are embracing visual analytics in …

Analyzing large, multivariate graphs is an important problem in many domains, yet such
graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree-table …

Visualizing multivariate networks is challenging because of the trade-offs necessary for
effectively encoding network topology and encoding the attributes associated with nodes …

Tabular visualization techniques integrate visual representations with tabular data to avoid
additional cognitive load caused by splitting users' attention. However, most of the existing …

Collections of natural extracts hold potential for the discovery of novel natural products with
original modes of action. The prioritization of extracts from collections remains challenging …

Molecular docking is a key technique in various fields like structural biology, medicinal
chemistry, and biotechnology. It is widely used for virtual screening during drug discovery …