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

CataAnno: An Ancient Catalog Annotator for Annotation Cleaning by Recommendation

Classical bibliography, by researching preserved catalogs from both official archives and personal collections of accumulated books, examines the books throughout history, thereby revealing cultural development across historical periods. In this work, we collaborate with domain experts to accomplish the task of data annotation concerning Chinese ancient catalogs. We introduce the CataAnno system that facilitates users in completing annotations more efficiently through cross-linked views, recommendation methods and convenient annotation interactions. The recommendation method can learn the background knowledge and annotation patterns that experts subconsciously integrate into the data during prior annotation processes. CataAnno searches for the most relevant examples previously annotated and recommends to the user. Meanwhile, the cross-linked views assist users in comprehending the correlations between entries and offer explanations for these recommendations. Evaluation and expert feedback confirm that the CataAnno system, by offering high-quality recommendations and visualizing the relationships between entries, can mitigate the necessity for specialized knowledge during the annotation process. This results in enhanced accuracy and consistency in annotations, thereby enhancing the overall efficiency.

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.e., differing collection methods, subjectivity of data interpretations and assumptions on temporal continuity. By analyzing the interactions of 14 archaeologists with different levels of domain expertise, we discovered that novices became more actively aware of typically overlooked data issues and domain experts became more confident of the visualization itself. We observed how participants quoted social factors to alleviate some uncertainty, while in order to minimize it they requested additional contextual breadth or depth of the data. While our visualization did not alleviate all uncertainty, we recognized how it sparked reflective meta-insights regarding methodological directions of the data. We believe our findings inform future visualizations on how to handle the complexity of implicit errors for a range of user typologies and for highly data-critical application domains such as the digital humanities.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

Evaluating a Taxonomy of Textual Uncertainty for Collaborative Visualisation in the Digital Humanities

The capture, modelling and visualisation of uncertainty has become a hot topic in many areas of science, such as the digital humanities (DH). Fuelled by critical voices among the DH community, DH scholars are becoming more aware of the intrinsic advantages that incorporating the notion of uncertainty into their workflows may bring. Additionally, the increasing availability of ubiquitous, web-based technologies has given rise to many collaborative tools that aim to support DH scholars in performing remote work alongside distant peers from other parts of the world. In this context, this paper describes two user studies seeking to evaluate a taxonomy of textual uncertainty aimed at enabling remote collaborations on digital humanities (DH) research objects in a digital medium. Our study focuses on the task of free annotation of uncertainty in texts in two different scenarios, seeking to establish the requirements of the underlying data and uncertainty models that would be needed to implement a hypothetical collaborative annotation system (CAS) that uses information visualisation and visual analytics techniques to leverage the cognitive effort implied by these tasks. To identify user needs and other requirements, we held two user-driven design experiences with DH experts and lay users, focusing on the annotation of uncertainty in historical recipes and literary texts. The lessons learned from these experiments are gathered in a series of insights and observations on how these different user groups collaborated to adapt an uncertainty taxonomy to solve the proposed exercises. Furthermore, we extract a series of recommendations and future lines of work that we share with the community in an attempt to establish a common agenda of DH research that focuses on collaboration around the idea of uncertainty.