Communicating Uncertainty in Digital Humanities Visualization Research.

Due to their historical nature, humanistic data encompass multiple sources of uncertainty. While humanists are accustomed to handling such uncertainty with their established methods, they are cautious of visualizations that appear overly objective and fail to communicate this uncertainty. To design more trustworthy visualizations for humanistic research, therefore, a deeper understanding of its relation to uncertainty is needed.

Implicit Error, Uncertainty and Confidence in Visualization: An Archaeological Case Study.

While we know that the visualization of quantifiable uncertainty impacts the confidence in insights, little is known about whether the same is true for uncertainty that originates from aspects so inherent to the data that they can only be accounted for qualitatively. Being embedded within an archaeological project, we realized how assessing such qualitative uncertainty is crucial in gaining a holistic and accurate understanding of regional spatio-temporal patterns of human settlements over millennia. We therefore investigated the impact of visualizing qualitative implicit errors on the sense-making process via a probe that deliberately represented three distinct implicit errors, i.

Introducing Layers of Meaning (LoM): A Framework to Reduce Semantic Distance of Visualization In Humanistic Research.

Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it.

Data Badges: Making an Academic Profile Through a DIY Wearable Physicalization.

In this pictorial, we present the design and making process of Data Badges as they were deployed during a one-week academic seminar. Data Badges are customizable physical conference badges that invite participants to make their own independent and personalized expressions of their academic profile by choosing and assembling a collection of predefined physical tokens on a flat wearable canvas. As our modular and intuitive design approach allows the construction to occur as a shared, collective activity, Data Badges take advantage of the creative, affective, and social values that underlie physicalization and its construction to engage participants in reflecting on personal data.

dendsort: modular leaf ordering methods for dendrogram representations in R.

Dendrograms are graphical representations of binary tree structures resulting from agglomerative hierarchical clustering. In Life Science, a cluster heat map is a widely accepted visualization technique that utilizes the leaf order of a dendrogram to reorder the rows and columns of the data table. The derived linear order is more meaningful than a random order, because it groups similar items together.

TrioVis: a visualization approach for filtering genomic variants of parent-child trios.

Summary: TrioVis is a visual analytics tool developed for filtering on coverage and variant frequency for genomic variants from exome sequencing of parent-child trios. In TrioVis, the variant data are organized by grouping each variant based on the laws of Mendelian inheritance. Taking three Variant Call Format files as input, TrioVis allows the user to test different coverage thresholds (i.