This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.

A growing body of work draws on feminist thinking to challenge assumptions about how people engage with and use visualizations. This work draws on feminist values, driving design and research guidelines that account for the influences of power and neglect. This prior work is largely prescriptive, however, forgoing articulation of how feminist theories of knowledge — or feminist epistemology — can alter research design and outcomes. At the core of our work is an engagement with feminist epistemology, drawing attention to how a new framework for how we know what we know enabled us to overcome intellectual tensions in our research. Specifically, we focus on the theoretical concept of entanglement, central to recent feminist scholarship, and contribute: a history of entanglement in the broader scope of feminist theory; an articulation of the main points of entanglement theory for a visualization context; and a case study of research outcomes as evidence of the potential of feminist epistemology to impact visualization research. This work answers a call in the community to embrace a broader set of theoretical and epistemic foundations and provides a starting point for bringing feminist theories into visualization research.

Foundational theories in visualization offer explanations and models for how people use visualizations to interpret data. A recent turn in visualization research has drawn attention to the ways in which these theories have limited explanatory power. Instead, researchers are drawing on alternative theories of knowledge, or epistemology, to address persistent problems in the field. This dissertation joins the conversation, presenting entanglement theory for visualization as an alternative epistemological theory and illustrative case studies that demonstrate how alternative theory in visualization shifts our attention and thus alters visualization research outcomes.

To support this claim, this dissertation presents two contributions that illustrate the productive capacity of feminist theory to contribute to visualization research. The first contribution is a novel theory for visualization transposed from feminist entanglement theory. Entanglement theory for visualization shifts the definitions of data, visualization, and insight toward relational and situated objects that are inseparable from feminist objects of concern: ethics, power, and privilege. Along with new definitions of data, visualization, and insights, we present three ways we mobilize feminist epistemology across visualization as the second contribution. The second contribution consists of three case studies demonstrating how feminist visualization theory drew our attention to previously ignored aspects of visualization design guidelines, the role of knowledge, and ethics in collaborative frameworks. Each case study illustrates the productive nature of approaching visualization research with an alternative epistemology.

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, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches . We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.

Visualization research methods help us study how visualization systems are used in complex real-world scenarios. One such widely used method is the interview — researchers asking participants specific questions to enrich their understanding. In this work, we introduce the pair-interview technique as a method that relies on two interviewers with specific and delineated roles, instead of one. Pair-interviewing focuses on the mechanics of conducting semi-structured interviews as a pair, and complements other existing visualization interview techniques. Based on a synthesis of the experiences and reflections of researchers in four diverse studies who used pair-interviewing, we outline recommendations for when and how to use pair-interviewing within visualization research studies.

In this provocation we ask the visualization research community to join us in removing chartjunk from our research lexicon. We present an etymology of chartjunk, framing its provocative origins as misaligned, and harmful, to the ways the term is currently used by visualization researchers. We call on the community to dissolve chartjunk from the ways we talk about, write about, and think about the graphical devices we design and study. As a step towards this goal we contribute a performance of maintenance through a trio of acts: editing the Wikipedia page on chartjunk, cutting out chartjunk from IEEE papers, and scanning and posting a repository of the pages with chartjunk removed to invite the community to re-imagine how we describe visualizations. This contribution blurs the boundaries between research, activism, and maintenance art, and is intended to inspire the community to join us in taking out the trash.