Visualizing event timelines for collaborative text writing is an important application for navigating and understanding such data, as time passes and the size and complexity of both text and timeline increase. They are often employed by applications such as code repositories and collaborative text editors. In this article, we present a visualization tool to explore historical records of writing of legislative texts, which were discussed and voted on by an assembly of representatives. Our visualization focuses on event timelines from text documents that involve multiple people and different topics, allowing for observation of different proposed versions of said text or tracking data provenance of given text sections, while highlighting the connections between all elements involved. We also describe the process of designing such a tool alongside domain experts, with three steps of evaluation being conducted to verify the effectiveness of our design.

This article presents a review and analysis of evaluation practices within the visualization and visual analytics (VIS) domain, with a focus on domain application work accepted at the IEEE VIS conference from 2018 to 2022. Through the analysis of 140 pertinent article, we establish a detailed classification principle for evaluation practices, using the Who, When, What, and How indicators. This principle covers facets such as analysis methods, targets, scenarios, participant expertise, and stages of occurrence. By systematically categorizing the application domains presented in these works, we apply our established classification principle to discern and categorize the evaluation practices within them, identifying the prevailing characteristics and trends. The article explores the variety of evaluation methods employed across different application domains and observes the distinctions in their usage. In conclusion, we provide insights and highlight concerns for conducting evaluations in upcoming domain application research. Our findings are intended to inform and guide subsequent studies in a similar context.

Abstract Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.

A music piece contains a large amount of information represented as a series of instructions corresponding to notes that must be played at specific times. These simple notes are combined to form complex harmonic structures that can be difficult to identify and analyze. Due to its simplicity and straightforward interpretation, music sheets and piano rolls have been the visual metaphor employed by most music visualization tools to support interpretation. Albeit it can represent all necessary elements to perform a music piece, these metaphors do not explicitly show many of the patterns and structures inherent to music arrangements, such as rhythm progression and harmonic interactions, needing users to create a mental model of them. Moreover, comparing different pieces and visualizing how a particular instrument track relates to the others is an issue not only for music sheet-based techniques, but also for most existing music visualization methods. In this paper, we present a novel visualization framework, called Music Overview, Stability, and Harmony Visualization (MoshViz), which facilitates the visualization and understanding of music renditions, focusing mainly on the visual analysis of specific musical instruments. Our approach creates a high-level model of music data and highlights structures of interest, enabling a detail+overview visualization to assist users in the task of identifying harmonic and melodic patterns. The usefulness and representativeness of MoshViz are confirmed by a set of user tests which demonstrate that the proposed visual metaphor matches, with a high degree of accuracy, the mental model of different users regarding the recognizable patterns of sounds.