In creating an academic paper, many hours of work are spread across planning, conducting research, analyzing findings, designing figures, and writing up the results. Ultimately, the effort can be obscured behind the final publication. For our recent paper for IEEE VIS, “Stitching Meaning: Practices of Data Textiles Creators,” we tracked every hour of work we did during its creation. Mirroring the topic of this paper, data textiles, where information is represented through fiber arts, we create a series of pieces using the data we collected on our time spent. Collectively, our ‘paper pillows’ encode three different views of our worktime in three textile mediums, representing our work as beautiful and practical objects. The act of making encouraged us to engage with our data from close and far perspectives that helped us to contextualize and reflect on our experiences, allowing us to live with and iteratively understand our data.

Tens of thousands of people have represented data by creating data-encoding textile pieces like blankets, scarves, and more. A prototypical example is the temperature blanket, which represents the weather through rows or blocks of different colors mapped to temperature ranges. While researchers have used fiber arts mediums to create exploratory projects, data visualization and physicalization research has largely not engaged with examples from this enormous and diverse community. We explore the space of data textiles, or fiber arts that encode information, by surveying creators (i.e., data fiber artists) on their projects and processes. We create a corpus of 159 examples of data textiles and present a schema characterizing the data encoding methods used in these projects. We also gather insights into creators' data workflows as well as their motives and discoveries through making with their data. Creators of data textiles use distinct processes to map their data, building fabric from component structures and substructures while using material properties like color and texture. From diverse data-tracking procedures, creators use and relate to data in varied ways. Working on these pieces also contributes to the creators' personal growth and data understanding. Our findings point to new opportunities for visualization, including opportunities to support fiber artists with tools formatted to their needs and opportunities to incorporate concepts from data textiles into other types of visualization (e.g., using texture, structural layouts, colorways).

Layered network visualizations assign each node to one of several parallel axes. They can convey sequence or flow data, hierarchies, or multiple data classes, but edge crossings and long edges often impair readability. Layout algorithms can reduce edge crossings and shorten edges using quick heuristics or optimal methods that prioritize human readability over computation speed. This work uses an optimization metaheuristic to provide the best of both worlds: high-quality layouts within a predetermined execution time. Our adaptation of the large neighborhood search (LNS) metaheuristic repeatedly selects fixed-sized subgraphs to lay out optimally. We conducted a computational evaluation using 450 synthetic networks to compare five ways of selecting candidate nodes, four ways of selecting their neighboring subgraph, and three criteria for determining subgraph size. LNS generally halved the number of crossings versus the barycentric heuristic while maintaining a reasonable runtime. Our best approach randomly selected candidate nodes, used degree centrality to pick cluster-like neighborhoods, and chose smaller neighborhoods that could be optimally laid out in 0.6 or 1.2 seconds (versus 6 seconds). In a case study visualizing 13 control flow graphs, most with over 1000 nodes, we show that our method can be employed to create visualizations with fewer crossings than Tabu Search, another metaheuristic, and vastly outperforms an ILP solver when runtime is bounded.

A layered graph is an important category of graph in which every node is assigned to a layer, and layers are drawn as parallel or radial lines. They are commonly used to display temporal data or hierarchical graphs. Previous research has demonstrated that minimizing edge crossings is the most important criterion to consider when looking to improve the readability of such graphs. While heuristic approaches exist for crossing minimization, we are interested in optimal approaches to the problem that prioritize human readability over computational scalability. We aim to improve the usefulness and applicability of such optimal methods by understanding and improving their scalability to larger graphs. This paper categorizes and evaluates the state-of-the-art linear programming formulations for exact crossing minimization and describes nine new and existing techniques that could plausibly accelerate the optimization algorithm. Through a computational evaluation, we explore each technique's effect on calculation time and how the techniques assist or inhibit one another, allowing researchers and practitioners to adapt them to the characteristics of their graphs. Our best-performing techniques yielded a median improvement of 2.5-17 × depending on the solver used, giving us the capability to create optimal layouts faster and for larger graphs. We provide an open-source implementation of our methodology in Python, where users can pick which combination of techniques to enable according to their use case. A free copy of this paper and all supplemental materials, datasets used, and source code are available at https://osf.io/5vq79.

Fused Deposition Modeling (FDM) is a low-cost method of 3D printing that involves stacking horizontal layers of plastic. FDM is used to produce tactile graphics and interfaces for people with visual impairments. Unfortunately, the print orientation can alter the structure and quality of braille and text. The difference between printing braille vertically and horizontally has been documented. However, we found no comprehensive study of these angles or the angles in between, nor any study providing a quantitative and qualitative user evaluation. We conducted two mixed-methods studies to evaluate the performance of braille printed at different angles. We measured reading time and subjective preference and performed a thematic analysis of participants’ responses. Our participants were faster using and preferred 75° and vertical braille over horizontal braille. These results provide makers with guidelines for creating models with readable 3D-printed braille.

Abstract Evaluations—encompassing computational evaluations, benchmarks and user studies—are essential tools for validating the performance and applicability of graph and network layout algorithms (also known as graph drawing). These evaluations not only offer significant insights into an algorithm's performance and capabilities, but also assist the reader in determining if the algorithm is suitable for a specific purpose, such as handling graphs with a high volume of nodes or dense graphs. Unfortunately, there is no standard approach for evaluating layout algorithms. Prior work holds a ‘Wild West’ of diverse benchmark datasets and data characteristics, as well as varied evaluation metrics and ways to report results. It is often difficult to compare layout algorithms without first implementing them and then running your own evaluation. In this systematic review, we delve into the myriad of methodologies employed to conduct evaluations—the utilized techniques, reported outcomes and the pros and cons of choosing one approach over another. Our examination extends beyond computational evaluations, encompassing user‐centric evaluations, thus presenting a comprehensive understanding of algorithm validation. This systematic review—and its accompanying website—guides readers through evaluation types, the types of results reported, and the available benchmark datasets and their data characteristics. Our objective is to provide a valuable resource for readers to understand and effectively apply various evaluation methods for graph layout algorithms. A free copy of this paper and all supplemental material is available at osf.io , and the categorized papers are accessible on our website at https://visdunneright.github.io/gd‐comp‐eval/ .