Derya Akbaba

Jen Rogers, Derya Akbaba, James Scott-Brown et al.

Decades of advocacy for reproducibility and replication have advanced open, transparent practices in the sciences. However, traditional notions of reproducibility fit poorly with design-oriented visualization research, where insights emerge through subjective, situated, and iterative work. So how can we ensure rigor and transparency in processes that are inherently unreproducible? To introduce transparency in design-oriented research, we propose to focus on traceability: surfacing the origin and development of research contributions based on rich sets of artifacts documenting the design process. We investigated traceability through a collaborative autoethnographic reflection that builds on several years of work exploring ways to make design-oriented research transparent. This exploration includes an experiment to build a tool to support traceability, which we called tRRRacer. The tRRRacer tool provided a testbed for us to operationalize the three tenets of a traceable process: (1) Record abundant, annotated artifacts representative of research activities; (2) Report curated research threads that articulate rationale and evolution of the process, allowing others to (3) Read via interfaces that help retrace claims and assess plausibility. Reflecting on our experiences, we contribute a theorization of traceability and reflections on how we might support it.

Derya Akbaba, Jack Wilburn, Main T. Nance et al.

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.

Haihan Lin, Derya Akbaba, Miriah Meyer et al.

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 over-confident in any findings if caveats are present. However, the implicit knowledge about the caveats of a dataset are typically not collected in a structured way, which is problematic especially when teams work together who might have knowledge about different aspects of a dataset. In this work, we define such analyst's knowledge about datasets as data hunches. We discuss the implications of data hunches and propose a set of techniques for recording and communicating data hunches through data visualization. Furthermore, we provide guidelines for designing visualizations that support recording and visualizing data hunches. 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.