We present GoTree, a declarative grammar allowing users to instantiate tree visualizations by specifying three aspects: visual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both "unit-decomposable" and "axis-decomposable" (terms we define). For tree visualizations within this subset, GoTree gives the user flexible and fine-grained control over the parameters of the techniques, supporting both explicit and implicit tree visualizations. We developed Tree Illustrator, an interactive authoring tool based on GoTree grammar. Tree Illustrator allows users to create a considerable number of tree visualizations, including not only existing techniques but also undiscovered and hybrid visualizations. We demonstrate the expressiveness and generative power of GoTree with a gallery of examples and conduct a qualitative study to validate the usability of Tree Illustrator.
Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.
https://doi.org/10.1145/3313831.3376381
Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across phases of data collection, wrangling, and modeling. As different choices can lead to diverging conclusions, understanding how researchers make analytic decisions is important for supporting robust and replicable analysis. In this study, we pore over nine published research studies and conduct semi-structured interviews with their authors. We observe that researchers often base their decisions on methodological or theoretical concerns, but subject to constraints arising from the data, expertise, or perceived interpretability. We confirm that researchers may experiment with choices in search of desirable results, but also identify other reasons why researchers explore alternatives yet omit findings. In concert with our interviews, we also contribute visualizations for communicating decision processes throughout an analysis. Based on our results, we identify design opportunities for strengthening end-to-end analysis, for instance via tracking and meta-analysis of multiple decision paths.
Data scientists experiment heavily with their code, compromising code quality to obtain insights faster. We observed ten data scientists perform hypothesis-driven data science tasks, and analyzed their coding, commenting, and analysis practice. We found that they have difficulty keeping track of their code experiments. When revisiting exploratory code to write production code later, they struggle to retrace their steps and capture the decisions made and insights obtained, and have to rerun code frequently. To address these issues, we designed TRACTUS, a system extending the popular RStudio IDE, that detects, tracks, and visualizes code experiments in hypothesis-driven data science tasks. TRACTUS helps recall decisions and insights by grouping code experiments into hypotheses, and structuring information like code execution output and documentation. Our user studies show how TRACTUS improves data scientists' workflows, and suggest additional opportunities for improvement. TRACTUS is available as an open source RStudio IDE addin at http://hci.rwth-aachen.de/tractus.
https://doi.org/10.1145/3313831.3376764
We investigate visualisations of networks on a 2-dimensional torus topology, like an opened-up and flattened doughnut. That is, the network is drawn on a rectangular area while "wrapping" specific links around the border. Previous work on torus drawings of networks has been mostly theoretical, limited to certain classes of networks, and not evaluated by human readability studies. We offer a simple interactive layout approach applicable to general graphs. We use this to find layouts affording better aesthetics in terms of conventional measures like more equal edge length and fewer crossings. In two controlled user studies we find that torus layout with either additional context or interactive panning provided significant performance improvement (in terms of error and time) over torus layout without either of these improvements, to the point that it is comparable to standard non-torus layout.
https://doi.org/10.1145/3313831.3376180