arXiv subjects

Miriah Meyer

Publications and source records attributed to Miriah Meyer.

18 recordsLinked to original sources

arXiv2026

Designing Vibes in a Science Museum: from @Science to @hugging_face

While feminist and critical data theories have long critiqued the use of data to uphold a positivist-informed view about science, few examples offer alternative methods to display scientific constructs. In response, we present Data and Me: an exhibit informed by feminist and critical data theories, which we designed and launched at a local science museum. Data and Me introduces museum visitors to data using a [AT]:hugging_face: vibe -- a vibe that signals that data can be [hashtag]slow, [hashtag]handmade, and [hashtag]personal. We designed this vibe to be noticeably different than the [AT]Science vibe in the rest of the museum. Throughout our design process, we adapted visualization vibes as an analytic and generative tool in the context of a science museum. We present four design choices that enable the design of a vibe: visual, topical, material, and crediting. We discuss how our exhibit aligns with ongoing discussions about alternative research outcomes and calls for plurality in HCI.

cs.HC↗

arXiv2026

Thinking Inside the Box: Considerations for Putting Data Physicalization Workshops in a Box

Visualization researchers utilize workshops both for applied research and to engage different populations with visualization-based activities. While there are many benefits to running visualization workshops, their utility and impact rely on the presence of a researcher who has deep knowledge about visualization theory and practice. In this work, we introduce workshop-in-a-box as a design concept intended to challenge the researcher-centric approach to data physicalization workshops. Through a design study with a socially innovative organization, we deployed several data physicalization workshops that our collaborator ran instead of us. Based on this experience, along with two accompanying case studies that validate the concept, we present material and procedural considerations for how to put data physicalization workshops into a box and the implications it has for extending visualization research outside the bounds of academia.

cs.HC↗

arXiv2026

Reflections on Traceability for Visualization Research

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.

cs.HC↗

arXiv2026

A Critical Reflection on the Values and Assumptions in Data Visualization

Visualization has matured into an established research field, producing widely adopted tools, design frameworks, and empirical foundations. As the field has grown, ideas from outside computer science have increasingly entered visualization discourse, questioning the fundamental values and assumptions on which visualization research stands. In this short position paper, we examine a set of values that we see underlying the seminal works of Jacques Bertin, John Tukey, Leland Wilkinson, Colin Ware, and Tamara Munzner. We articulate three prominent values in these texts - universality, objectivity, and efficiency - and examine how these values permeate visualization tools, curricula, and research practices. We situate these values within a broader set of critiques that call for more diverse priorities and viewpoints. By articulating these tensions, we call for our community to embrace a more pluralistic range of values to shape our future visualization tools and guidelines.

cs.HC↗

arXiv2025

Manifesto for Putting 'Chartjunk' in the Trash 2021!

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.

cs.HC↗

arXiv2022

Data Bricks Space Mission: Teaching Kids about Data with Physicalization

The Data Bricks Space Mission is a prototype activity based on data physicalization for teaching kids about data. The design of the activity is based on a literature review and interviews with elementary school teachers, and targets kids aged 10-12. Using Lego bricks and a fictional space adventure story, teachers can use the Data Bricks Space Mission activity to empower kids to produce data, communicate their findings, and gain a better understanding of the relationship between data and the world around them.

physics.ed-ph↗

arXiv2022

Data Hunches: Incorporating Personal Knowledge into Visualizations

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.

cs.HC↗

arXiv2021

An interview method for engaging personal data

Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal data without first understanding what users want to do with their data. Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data. We present a new method that directly tackles this challenge: the data engagement interview. This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants the opportunity to directly engage with their data, and interviewers to observe and ask questions throughout this engagement. We describe the method's development through a case study with asthmatic participants, share insights and guidance from our experience, and report a broad set of insights from these interviews.

cs.HC↗

arXiv2021

Exploring the Personal Informatics Analysis Gap: "There's a Lot of Bacon"

Personal informatics research helps people track personal data for the purposes of self-reflection and gaining self-knowledge. This field, however, has predominantly focused on the data collection and insight-generation elements of self-tracking, with less attention paid to flexible data analysis. As a result, this inattention has led to inflexible analytic pipelines that do not reflect or support the diverse ways people want to engage with their data. This paper contributes a review of personal informatics and visualization research literature to expose a gap in our knowledge for designing flexible tools that assist people engaging with and analyzing personal data in personal contexts, what we call the personal informatics analysis gap. We explore this gap through a multistage longitudinal study on how asthmatics engage with personal air quality data, and we report how participants: were motivated by broad and diverse goals; exhibited patterns in the way they explored their data; engaged with their data in playful ways; discovered new insights through serendipitous exploration; and were reluctant to use analysis tools on their own. These results present new opportunities for visual analysis research and suggest the need for fundamental shifts in how and what we design when supporting personal data analysis.

cs.HC↗

arXiv2020

Insights From Experiments With Rigor in an EvoBio Design Study

Design study is an established approach of conducting problem-driven visualization research. The academic visualizationcommunity has produced a large body of work for reporting on design studies, informed by a handful of theoretical frameworks, andapplied to a broad range of application areas. The result is an abundance of reported insights into visualization design, with anemphasis on novel visualization techniques and systems as the primary contribution of these studies. In recent work we proposeda new, interpretivist perspective on design study and six companion criteria for rigor that highlight the opportunities for researchersto contribute knowledge that extends beyond visualization idioms and software. In this work we conducted a year-long collaborationwith evolutionary biologists to develop an interactive tool for visual exploration of multivariate datasets and phylogenetic trees. Duringthis design study we experimented with methods to support three of the rigor criteria:ABUNDANT,REFLEXIVE, andTRANSPARENT. As aresult we contribute two novel visualization techniques for the analysis of multivariate phylogenetic datasets, three methodologicalrecommendations for conducting design studies drawn from reflections over our process of experimentation, and two writing devices forreporting interpretivist design study. We offer this work as an example for implementing the rigor criteria to produce a diverse range ofknowledge contributions.

cs.HC↗

arXiv2019

Criteria for Rigor in Visualization Design Study

We develop a new perspective on research conducted through visualization design study that emphasizes design as a method of inquiry and the broad range of knowledge-contributions achieved through it as multiple, subjective, and socially constructed. From this interpretivist position we explore the nature of visualization design study and develop six criteria for rigor. We propose that rigor is established and judged according to the extent to which visualization design study research and its reporting are INFORMED, REFLEXIVE, ABUNDANT, PLAUSIBLE, RESONANT, and TRANSPARENT. This perspective and the criteria were constructed through a four-year engagement with the discourse around rigor and the nature of knowledge in social science, information systems, and design. We suggest methods from cognate disciplines that can support visualization researchers in meeting these criteria during the planning, execution, and reporting of design study. Through a series of deliberately provocative questions, we explore implications of this new perspective for design study research in visualization, concluding that as a discipline, visualization is not yet well positioned to embrace, nurture, and fully benefit from a rigorous, interpretivist approach to design study. The perspective and criteria we present are intended to stimulate dialogue and debate around the nature of visualization design study and the broader underpinnings of the discipline.

cs.HC↗

arXiv2019

Origraph: Interactive Network Wrangling

Networks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline, yet interactive network wrangling has received little attention in the visualization research community. In this paper, we discuss a set of operations that are important for wrangling network datasets and introduce a visual data wrangling tool, Origraph, that enables analysts to apply these operations to their datasets. Key operations include creating a network from source data such as tables, reshaping a network by introducing new node or edge classes, filtering nodes or edges, and deriving new node or edge attributes. Our tool, Origraph, enables analysts to execute these operations with little to no programming, and to immediately visualize the results. Origraph provides views to investigate the network model, a sample of the network, and node and edge attributes. In addition, we introduce interfaces designed to aid analysts in specifying arguments for sensible network wrangling operations. We demonstrate the usefulness of Origraph in two Use Cases: first, we investigate gender bias in the film industry, and then the influence of money on the political support for the war in Yemen.

cs.HC↗

arXiv2018

Reflection On Reflection In Design Study

Visualization design study research methodologies emphasize the need for reflection to generate knowledge. And yet, there is very little guidance in the literature specifying what reflection in the context of design studies actually involves. We initiated a community discussion on this topic through a panel at the 2017 IEEE VIS Conference - this report documents the panel discussion. We analyze the panel content through the lense of our own reflective experiences and propose several priorities for ongoing thinking on reflection in applied visualization research.

cs.HC↗

arXiv2018

A Framework for Creative-Visualization Opportunities Workshops

Applied visualization researchers often work closely with domain collaborators to explore new and useful applications of visualization. The early stages of collaborations are typically time consuming for all stakeholders as researchers piece together an understanding of domain challenges from disparate discussions and meetings. A number of recent projects, however, report on the use of creative visualization-opportunities (CVO) workshops to accelerate the early stages of applied work, eliciting a wealth of requirements in a few days of focused work. Yet, there is no established guidance for how to use such workshops effectively. In this paper, we present the results of a 2-year collaboration in which we analyzed the use of 17 workshops in 10 visualization contexts. Its primary contribution is a framework for CVO workshops that: 1) identifies a process model for using workshops; 2) describes a structure of what happens within effective workshops; 3) recommends 25 actionable guidelines for future workshops; and 4) presents an example workshop and workshop methods. The creation of this framework exemplifies the use of critical reflection to learn about visualization in practice from diverse studies and experience.

cs.HC↗

arXiv2017

Worksheets for Guiding Novices through the Visualization Design Process

For visualization pedagogy, an important but challenging notion to teach is design, from making to evaluating visualization encodings, user interactions, or data visualization systems. In our previous work, we introduced the design activity framework to codify the high-level activities of the visualization design process. This framework has helped structure experts' design processes to create visualization systems, but the framework's four activities lack a breakdown into steps with a concrete example to help novices utilizing this framework in their own real-world design process. To provide students with such concrete guidelines, we created worksheets for each design activity: understand, ideate, make, and deploy. Each worksheet presents a high-level summary of the activity with actionable, guided steps for a novice designer to follow. We validated the use of this framework and the worksheets in a graduate-level visualization course taught at our university. For this evaluation, we surveyed the class and conducted 13 student interviews to garner qualitative, open-ended feedback and suggestions on the worksheets. We conclude this work with a discussion and highlight various areas for future work on improving visualization design pedagogy.

cs.HC↗

arXiv2017

EpiFi: An In-Home Sensor Network Architecture for Epidemiological Studies

We design and build a system called EpiFi, which allows epidemiologists to easily design and deploy experiments in homes. The focus of EpiFi is reducing the barrier to entry for deploying and using an in-home sensor network. We present a novel architecture for in-home sensor networks configured using a single configuration file and provide: a fast and reliable method for device discovery when installed in the home, a new mechanism for sensors to authenticate over the air using a subject's home WiFi router, and data reliability mechanisms to minimize loss in the network through a long-term deployment. We work collaboratively with pediatric asthma researchers to design three studies and deploy EpiFi in homes.

cs.NI↗

arXiv2017

Graffinity: Visualizing Connectivity In Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.

cs.GR↗

arXiv2013

Rethinking Abstractions for Big Data: Why, Where, How, and What

Big data refers to large and complex data sets that, under existing approaches, exceed the capacity and capability of current compute platforms, systems software, analytical tools and human understanding. Numerous lessons on the scalability of big data can already be found in asymptotic analysis of algorithms and from the high-performance computing (HPC) and applications communities. However, scale is only one aspect of current big data trends; fundamentally, current and emerging problems in big data are a result of unprecedented complexity--in the structure of the data and how to analyze it, in dealing with unreliability and redundancy, in addressing the human factors of comprehending complex data sets, in formulating meaningful analyses, and in managing the dense, power-hungry data centers that house big data. The computer science solution to complexity is finding the right abstractions, those that hide as much triviality as possible while revealing the essence of the problem that is being addressed. The "big data challenge" has disrupted computer science by stressing to the very limits the familiar abstractions which define the relevant subfields in data analysis, data management and the underlying parallel systems. As a result, not enough of these challenges are revealed by isolating abstractions in a traditional software stack or standard algorithmic and analytical techniques, and attempts to address complexity either oversimplify or require low-level management of details. The authors believe that the abstractions for big data need to be rethought, and this reorganization needs to evolve and be sustained through continued cross-disciplinary collaboration.

cs.GL↗