42-Issue 3

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https://diglib.eg.org/handle/10.2312/2633338

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Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bludau, Mark-Jan; Dörk, Marian; Tominski, Christian; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose 'unfolding of edges' as an interactive approach to integrate multivariate edge attributes dynamically into existing node-link diagrams. Unfolding edges is an in-situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on-demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web-based prototypes were implemented based on which we conducted interviews with domain experts. The experts' feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.
VENUS: A Geometrical Representation for Quantum State Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Ruan, Shaolun; Yuan, Ribo; Guan, Qiang; Lin, Yanna; Mao, Ying; Jiang, Weiwen; Wang, Zhepeng; Xu, Wei; Wang, Yong; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.
Memory-Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Zellmann, Stefan; Wu, Qi; Ma, Kwan-Liu; Wald, Ingo; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
A common way to render cell-centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high-quality interpolator, the memory requirements of the dual mesh data structure are significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU-friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off-the-shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding a gridlet element type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state-of-the-art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory-efficient.
Process and Pitfalls of Online Teaching and Learning with Design Study ''Lite'' Methodology: A Retrospective Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Syeda, Uzma Haque; Dunne, Cody; Borkin, Michelle A.; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study ''Lite'' Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in-person data visualization courses with 30 students or less and was implemented in conjunction with Service-Learning (S-L).With the growth and popularity of the data visualization field-and the teaching environment created by the COVID-19 pandemic-more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service-Learning (S-L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S-L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom.
Beyond Alternative Text and Tables: Comparative Analysis of Visualization Tools and Accessibility Methods
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Kim, Nam Wook; Ataguba, Grace; Joyner, Shakila Cherise; Zhao, Chuangdian; Im, Hyejin; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low-vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off-the-shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.
RectEuler: Visualizing Intersecting Sets using Rectangles
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Paetzold, Patrick; Kehlbeck, Rebecca; Strobelt, Hendrik; Xue, Yumeng; Storandt, Sabine; Deussen, Oliver; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Euler diagrams are a popular technique to visualize set-typed data. However, creating diagrams using simple shapes remains a challenging problem for many complex, real-life datasets. To solve this, we propose RectEuler: a flexible, fully-automatic method using rectangles to create Euler-like diagrams. We use an efficient mixed-integer optimization scheme to place set labels and element representatives (e.g., text or images) in conjunction with rectangles describing the sets. By defining appropriate constraints, we adhere to well-formedness properties and aesthetic considerations. If a dataset cannot be created within a reasonable time or at all, we iteratively split the diagram into multiple components until a drawable solution is found. Redundant encoding of the set membership using dots and set lines improves the readability of the diagram. Our web tool lets users see how the layout changes throughout the optimization process and provides interactive explanations. For evaluation, we perform quantitative and qualitative analysis across different datasets and compare our method to state-of-the-art Euler diagram generation methods.
visMOP - A Visual Analytics Approach for Multi-omics Pathways
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Brich, Nicolas; Schacherer, Nadine; Hoene, Miriam; Weigert, Cora; Lehmann, Rainer; Krone, Michael; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
We present an approach for the visual analysis of multi-omics data obtained using high-throughput methods. The term ''omics'' denotes measurements of different types of biologically relevant molecules, like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics). Current popular visualization approaches often only support analyzing each of these omics separately. This, however, disregards the interconnectedness of different biologically relevant molecules and processes. Consequently, it describes the actual events in the organism suboptimally or only partially. Our visual analytics approach for multi-omics data provides a comprehensive overview and details-on-demand by integrating the different omics types in multiple linked views. To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering. To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real-world transcriptomics, proteomics, and lipidomics measurements from mice.
A Comparative Evaluation of Visual Summarization Techniques for Event Sequences
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Zinat, Kazi Tasnim; Yang, Jinhua; Gandhi, Arjun; Mitra, Nistha; Liu, Zhicheng; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Real-world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed numerous visual summarization techniques to generate concise overviews of sequential data. These techniques vary widely in terms of summary structures and contents, and currently there is a knowledge gap in understanding the effectiveness of these techniques. In this work, we present the design and results of an insight-based crowdsourcing experiment evaluating three existing visual summarization techniques: CoreFlow, SentenTree, and Sequence Synopsis. We compare the visual summaries generated by these techniques across three tasks, on six datasets, at six levels of granularity. We analyze the effects of these variables on summary quality as rated by participants and completion time of the experiment tasks. Our analysis shows that Sequence Synopsis produces the highest-quality visual summaries for all three tasks, but understanding Sequence Synopsis results also takes the longest time. We also find that the participants evaluate visual summary quality based on two aspects: content and interpretability. We discuss the implications of our findings on developing and evaluating new visual summarization techniques.
FlexEvent: going beyond Case-Centric Exploration and Analysis of Multivariate Event Sequences
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Linden, Sanne van der; Wulterkens, Bernice M.; Gilst, Merel M. van; Overeem, Sebastiaan; Pul, Carola van; Vilanova, Anna; Elzen, Stef van den; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives.
Teru Teru Bozu: Defensive Raincloud Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Correll, Michael; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s [APW*19] ''raincloud plots.'' In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually ''defend'' against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of ''defensive'' raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.
Evaluating View Management for Situated Visualization in Web-based Handheld AR
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Batch, Andrea; Shin, Sungbok; Liu, Julia; Butcher, Peter W. S.; Ritsos, Panagiotis D.; Elmqvist, Niklas; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
As visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user's view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world-in-miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web-based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.
Exploring Interpersonal Relationships in Historical Voting Records
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Cantareira, Gabriel Dias; Xing, Yiwen; Cole, Nicholas; Borgo, Rita; Abdul-Rahman, Alfie; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.
Do Disease Stories need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Mittenentzwei, Sarah; Weiß, Veronika; Schreiber, Stefanie; Garrison, Laura A.; Bruckner, Stefan; Pfister, Malte; Preim, Bernhard; Meuschke, Monique; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.
GO-Compass: Visual Navigation of Multiple Lists of GO terms
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Harbig, Theresa; Witte Paz, Mathias; Nieselt, Kay; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Analysis pipelines in genomics, transcriptomics, and proteomics commonly produce lists of genes, e.g., differentially expressed genes. Often these lists overlap only partly or not at all and contain too many genes for manual comparison. However, using background knowledge, such as the functional annotations of the genes, the lists can be abstracted to functional terms. One approach is to run Gene Ontology (GO) enrichment analyses to determine over- and/or underrepresented functions for every list of genes. Due to the hierarchical structure of the Gene Ontology, lists of enriched GO terms can contain many closely related terms, rendering the lists still long, redundant, and difficult to interpret for researchers. In this paper, we present GO-Compass (Gene Ontology list comparison using Semantic Similarity), a visual analytics tool for the dispensability reduction and visual comparison of lists of GO terms. For dispensability reduction, we adapted the REVIGO algorithm, a summarization method based on the semantic similarity of GO terms, to perform hierarchical dispensability clustering on multiple lists. In an interactive dashboard, GO-Compass offers several visualizations for the comparison and improved interpretability of GO terms lists. The hierarchical dispensability clustering is visualized as a tree, where users can interactively filter out dispensable GO terms and create flat clusters by cutting the tree at a chosen dispensability. The flat clusters are visualized in animated treemaps and are compared using a correlation heatmap, UpSet plots, and bar charts. With two use cases on published datasets from different omics domains, we demonstrate the general applicability and effectiveness of our approach. In the first use case, we show how the tool can be used to compare lists of differentially expressed genes from a transcriptomics pipeline and incorporate gene information into the analysis. In the second use case using genomics data, we show how GO-Compass facilitates the analysis of many hundreds of GO terms. For qualitative evaluation of the tool, we conducted feedback sessions with five domain experts and received positive comments. GO-Compass is part of the Tue- Vis Visualization Server as a web application available at https://go-compass-tuevis.cs.uni-tuebingen.de/
Tac-Anticipator: Visual Analytics of Anticipation Behaviors in Table Tennis Matches
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Wang, Jiachen; Wu, Yihong; Zhang, Xiaolong; Zeng, Yixin; Zhou, Zheng; Zhang, Hui; Xie, Xiao; Wu, Yingcai; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in-lab behaviors, failed to capture the characteristics of in-situ anticipation behaviors in real matches. This research proposes a data-driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac-Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.
Visual Analytics on Network Forgetting for Task-Incremental Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Li, Ziwei; Xu, Jiayi; Chao, Wei-Lun; Shen, Han-Wei; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Task-incremental learning (Task-IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under-explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in-depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.
ChemoGraph: Interactive Visual Exploration of the Chemical Space
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Kale, Bharat; Clyde, Austin; Sun, Maoyuan; Ramanathan, Arvind; Stevens, Rick; Papka, Michael E.; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
Mini-VLAT: A Short and Effective Measure of Visualization Literacy
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Pandey, Saugat; Ottley, Alvitta; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants' Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.
DASS Good: Explainable Data Mining of Spatial Cohort Data
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Wentzel, Andrew; Floricel, Carla; Canahuate, Guadalupe; Naser, Mohamed A.; Mohamed, Abdallah S.; Fuller, Clifton David; Dijk, Lisanne van; Marai, G. Elisabeta; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.
Ferret: Reviewing Tabular Datasets for Manipulation
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Lange, Devin; Sahai, Shaurya; Phillips, Jeff M.; Lex, Alexander; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
How do we ensure the veracity of science? The act of manipulating or fabricating scientifc data has led to many high-profle fraud cases and retractions. Detecting manipulated data, however, is a challenging and time-consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically fagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer-review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these fndings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.

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