Untidy Data: The Unreasonable Effectiveness of Tables

"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective

The visualization community has a rich history of reflecting upon visualization design flaws. Although research in this area has remained lively, we believe it is essential to continuously revisit this classic and critical topic in visualization research by incorporating more empirical evidence from diverse sources, characterizing new design flaws, building more systematic theoretical frameworks, and understanding the underlying reasons for these flaws. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur. Specifically, we analyzed 2227 flawed data visualizations collected from an online gallery and derived a design task-associated taxonomy containing 76 specific design flaws. These flaws were further classified into three high-level categories (i.e., misinformation, uninformativeness, unsociability) and ten subcategories (e.g., inaccuracy, unfairness, ambiguity). Next, we organized five focus groups to explore why these design flaws occur and identified seven causes of the flaws. Finally, we proposed a research agenda for combating visualization design flaws and summarize nine research opportunities.

The Effect of Visual Aids on Reading Numeric Data Tables

Data tables are one of the most common ways in which people encounter data. Although mostly built with text and numbers, data tables have a spatial layout and often exhibit visual elements meant to facilitate their reading. Surprisingly, there is an empirical knowledge gap on how people read tables and how different visual aids affect people's reading of tables. In this work, we seek to address this vacuum through a controlled study. We asked participants to repeatedly perform four different tasks with four table representation conditions (plain tables, tables with zebra striping, tables with cell background color encoding cell value, and tables with in-cell bars with lengths encoding cell value). We analyzed completion time, error rate, gaze-tracking data, mouse movement and participant preferences. We found that color and bar encodings help for finding maximum values. For a more complex task (comparison of proportional differences) color and bar helped less than zebra striping. We also characterize typical human behavior for the four tasks. These findings inform the design of tables and research directions for improving presentation of data in tabular form.

Transitioning to a Commercial Dashboarding System: Socio-Technical Observations and Opportunities

Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems. Although a number of studies have looked at the technical challenges of adopting these systems, very few have focused on the socio-technical issues that arise. In this paper, we report on the results of an interview study with 17 participants working in a range of roles at a long-established, traditional manufacturing company as they adopted Microsoft Power BI. The results highlight a number of socio-technical challenges the employees faced, including difficulties in training, using and creating dashboards, and transitioning to a modern digital company. Based on these results, we propose a number of opportunities for both companies and visualization researchers to improve these difficult transitions, as well as opportunities for rethinking how we design dashboarding systems for real-world use.

From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making

In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the "intelligence" stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely "design" and "choice"? To further explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess whether and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges that decision-focused visualization tools face in realizing their full potential to support all stages of the decision making process. It reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Based on our findings, to better support the choice stage, future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility I evels. Our curated list of the 88 surveyed visualization tools is available in the OSF link (https://osf.io/nrasz/?view_only=b92a90a34ae241449b5f2cd33383bfcb).

Identifying Visualization Opportunities to Help Architects Manage the Complexity of Building Codes

We report a study investigating the viability of using interactive visualizations to aid architectural design with building codes. While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, we found that interactive visualizations have promising potential to aid design exploration and sensemaking in early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes. To tackle these challenges, we propose various user-driven knowledge management mechanisms for integrating, negotiating, interpreting, and documenting building code rules.

The Challenge of Interdisciplinarity at the Intersection of Groundwater Management and Visualization Research

This design study presents an analysis and abstraction of temporal and spatial data, and workflows in the domain of hydrogeology and the design and development of an interactive visualization prototype. Developed in close collaboration with a group of hydrogeological researchers, the interface supports them in data exploration, selection of data for their numerical model calibration, and communication of findings to their industry partners. We highlight both pitfalls and learnings of the iterative design and validation process and explore the role of rapid prototyping. Some of the main lessons were that the ability to see their own data changed the engagement of skeptical users dramatically and that interactive rapid prototyping tools are thus powerful to unlock the advantage of visual analysis for novice users. Further, we observed that the process itself helped the domain scientists understand the potential and challenges of their data more than the final interface prototype.

Visualizing the Scripts of Data Wrangling With Somnus

Data workers use various scripting languages for data transformation, such as SAS, R, and Python. However, understanding intricate code pieces requires advanced programming skills, which hinders data workers from grasping the idea of data transformation at ease. Program visualization is beneficial for debugging and education and has the potential to illustrate transformations intuitively and interactively. In this article, we explore visualization design for demonstrating the semantics of code pieces in the context of data transformation. First, to depict individual data transformations, we structure a design space by two primary dimensions, i.e., key parameters to encode and possible visual channels to be mapped. Then, we derive a collection of 23 glyphs that visualize the semantics of transformations. Next, we design a pipeline, named Somnus, that provides an overview of the creation and evolution of data tables using a provenance graph. At the same time, it allows detailed investigation of individual transformations. User feedback on Somnus is positive. Our study participants achieved better accuracy with less time using Somnus, and preferred it over carefully-crafted textual description. Further, we provide two example applications to demonstrate the utility and versatility of Somnus.

Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations

Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications. A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the inherent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot easily be embedded in a tabular layout. In Fuzzy Spreadsheet, a cell can hold and display a distribution of values. This integrated uncertainty-handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but also with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses. Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells. To evaluate its usability and the perceived mental effort required, we conducted a user study. The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort in almost all tasks tested.

Revealing the Semantics of Data Wrangling Scripts With Comantics

Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop Comantics, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a "slot filling" strategy. We design experiments to evaluate the performance of Comantics. Further, we assess its flexibility using three example applications in different domains.

Rigel: Transforming Tabular Data by Declarative Mapping

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness.

Finding Their Data Voice: Practices and Challenges of Dashboard Users

Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers' practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.

Notebooks for Data Analysis and Visualization: Moving Beyond the Data

Notebooks are a relatively new way of analyzing data and creating visualizations. They differ from the common graphical user interfaces used for visualization tools in many ways, and have their own strengths and weaknesses. In particular, they allow easy sharing, experimentation, and collaboration, and provide context about the data for different kinds of users. They also integrate modeling, forecasting, and complex analyses directly with the visualization. We believe that notebooks provide a unique and fundamentally new way of working with and understanding data. By laying out their unique properties, we hope to inspire both researchers and practitioners to investigate their many uses, explore their pros and cons, and share their findings.

Tabular strategies for metadata in ecology, evolution, and the environmental sciences

Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated-even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy-to-read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights.

MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks

Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).