Applying Nuclear Forward Scattering as In Situ and Operando Tool for the Characterization of FeN4 Moieties in the Hydrogen Evolution Reaction.

Nuclear forward scattering (NFS) is a synchrotron-based technique relying on the recoil-free nuclear resonance effect similar to Mössbauer spectroscopy. In this work, we introduce NFS for in situ and operando measurements during electrocatalytic reactions. The technique enables faster data acquisition and better discrimination of certain iron sites in comparison to Mössbauer spectroscopy. It is directly accessible at various synchrotrons to a broad community of researchers and is applicable to multiple metal isotopes. We demonstrate the power of this technique with the hydrogen evolution mechanism of an immobilized iron porphyrin supported on carbon. Such catalysts are often considered as model systems for iron-nitrogen-carbon (FeNC) catalysts. Using in situ and operando NFS in combination with theoretical predictions of spectroscopic data enables the identification of the intermediate that is formed prior to the rate-determining step. The conclusions on the reaction mechanism can be used for future optimization of immobilized molecular catalysts and metal-nitrogen-carbon (MNC) catalysts.

Mesoporous CuFe2 O4 Photoanodes for Solar Water Oxidation: Impact of Surface Morphology on the Photoelectrochemical Properties.

Metal oxide-based photoelectrodes for solar water splitting often utilize nanostructures to increase the solid-liquid interface area. This reduces charge transport distances and increases the photocurrent for materials with short minority charge carrier diffusion lengths. While the merits of nanostructuring are well established, the effect of surface order on the photocurrent and carrier recombination has not yet received much attention in the literature. To evaluate the impact of pore ordering on the photoelectrochemical properties, mesoporous CuFe2 O4 (CFO) thin film photoanodes were prepared by dip-coating and soft-templating. Here, the pore order and geometry can be controlled by addition of copolymer surfactants poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (Pluronic® F-127), polyisobutylene-block-poly(ethylene oxide) (PIB-PEO) and poly(ethylene-co-butylene)-block-poly(ethylene oxide) (Kraton liquid™-PEO, KLE). The non-ordered CFO showed the highest photocurrent density of 0.2 mA/cm2 at 1.3 V vs. RHE for sulfite oxidation, but the least photocurrent density for water oxidation. Conversely, the ordered CFO presented the best photoelectrochemical water oxidation performance. These differences can be understood on the basis of the high surface area, which promotes hole transfer to sulfite (a fast hole acceptor), but retards oxidation of water (a slow hole acceptor) due to electron-hole recombination at the defective surface. This interpretation is confirmed by intensity-modulated photocurrent (IMPS) and vibrating Kelvin probe surface photovoltage spectroscopy (VKP-SPS). The lowest surface recombination rate was observed for the ordered KLE-based mesoporous CFO, which retains spherical pore shapes at the surface resulting in fewer surface defects. Overall, this work shows that the photoelectrochemical energy conversion efficiency of copper ferrite thin films is not just controlled by the surface area, but also by surface order.

Mosaic: An Architecture for Scalable & Interoperable Data Views.

Mosaic is an architecture for greater scalability, extensibility, and interoperability of interactive data views. Mosaic decouples data processing from specification logic: clients publish their data needs as declarative queries that are then managed and automatically optimized by a coordinator that proxies access to a scalable data store. Mosaic generalizes Vegalite's selection abstraction to enable rich integration and linking across visualizations and components such as menus, text search, and tables. We demonstrate Mosaic's expressiveness, extensibility, and interoperability through examples that compose diverse visualization, interaction, and optimization techniques-many constructed using vgplot, a grammar of interactive graphics in which graphical marks act as Mosaic clients. To evaluate scalability, we present benchmark studies with order-of-magnitude performance improvements over existing web-based visualization systems-enabling flexible, real-time visual exploration of billion+ record datasets. We conclude by discussing Mosaic's potential as an open platform that bridges visualization languages, scalable visualization, and interactive data systems more broadly.

Data Navigator: An Accessibility-Centered Data Navigation Toolkit.

Making data visualizations accessible for people with disabilities remains a significant challenge in current practitioner efforts. Existing visualizations often lack an underlying navigable structure, fail to engage necessary input modalities, and rely heavily on visual-only rendering practices. These limitations exclude people with disabilities, especially users of assistive technologies. To address these challenges, we present Data Navigator: a system built on a dynamic graph structure, enabling developers to construct navigable lists, trees, graphs, and flows as well as spatial, diagrammatic, and geographic relations. Data Navigator supports a wide range of input modalities: screen reader, keyboard, speech, gesture detection, and even fabricated assistive devices. We present 3 case examples with Data Navigator, demonstrating we can provide accessible navigation structures on top of raster images, integrate with existing toolkits at scale, and rapidly develop novel prototypes. Data Navigator is a step towards making accessible data visualizations easier to design and implement.

Dead or Alive: Continuous Data Profiling for Interactive Data Science.

Profiling data by plotting distributions and analyzing summary statistics is a critical step throughout data analysis. Currently, this process is manual and tedious since analysts must write extra code to examine their data after every transformation. This inefficiency may lead to data scientists profiling their data infrequently, rather than after each transformation, making it easy for them to miss important errors or insights. We propose continuous data profiling as a process that allows analysts to immediately see interactive visual summaries of their data throughout their data analysis to facilitate fast and thorough analysis. Our system, AutoProfiler, presents three ways to support continuous data profiling: (1) it automatically displays data distributions and summary statistics to facilitate data comprehension; (2) it is live, so visualizations are always accessible and update automatically as the data updates; (3) it supports follow up analysis and documentation by authoring code for the user in the notebook. In a user study with 16 participants, we evaluate two versions of our system that integrate different levels of automation: both automatically show data profiles and facilitate code authoring, however, one version updates reactively ("live") and the other updates only on demand ("dead"). We find that both tools, dead or alive, facilitate insight discovery with 91% of user-generated insights originating from the tools rather than manual profiling code written by users. Participants found live updates intuitive and felt it helped them verify their transformations while those with on-demand profiles liked the ability to look at past visualizations. We also present a longitudinal case study on how AutoProfiler helped domain scientists find serendipitous insights about their data through automatic, live data profiles. Our results have implications for the design of future tools that offer automated data analysis support.

Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability.

We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line. We found this effect across two preregistered experiments with 140 and 420 participants. These experiments also show that the bias is reduced when using a dot encoding of the same series. We can model the bias with the average of the data series and the average of the points drawn along the line. This bias might arise because higher variability leads to stronger weighting in the average calculation, either due to the longer line segments (even though those segments contain the same number of data values) or line segments with higher variability being otherwise more visually salient. Understanding and predicting this bias is important for visualization design guidelines, recommendation systems, and tool builders, as the bias can adversely affect estimates of averages and trends.

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments.

To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including tracking many compression experiments, identifying subtle changes in model behavior, and negotiating complex accuracy-efficiency trade-offs. However, existing compression tools poorly support comparison, leading to tedious and, sometimes, incomplete analyses spread across disjoint tools. To support real-world comparative workflows, we develop an interactive visual system called COMPRESS AND COMPARE. Within a single interface, COMPRESS AND COMPARE surfaces promising compression strategies by visualizing provenance relationships between compressed models and reveals compression-induced behavior changes by comparing models' predictions, weights, and activations. We demonstrate how COMPRESS AND COMPARE supports common compression analysis tasks through two case studies, debugging failed compression on generative language models and identifying compression artifacts in image classification models. We further evaluate COMPRESS AND COMPARE in a user study with eight compression experts, illustrating its potential to provide structure to compression workflows, help practitioners build intuition about compression, and encourage thorough analysis of compression's effect on model behavior. Through these evaluations, we identify compression-specific challenges that future visual analytics tools should consider and COMPRESS AND COMPARE visualizations that may generalize to broader model comparison tasks..

Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco.

Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco-a framework to model visualization knowledge-to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in existing graphical perception literature informing recommendation algorithms, (2) cluster papers by their preferred design rules and constraints, and (3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.

Studying Early Decision Making with Progressive Bar Charts.

We conduct a user study to quantify and compare user performance for a value comparison task using four bar chart designs, where the bars show the mean values of data loaded progressively and updated every second (progressive bar charts). Progressive visualization divides different stages of the visualization pipeline-data loading, processing, and visualization-into iterative animated steps to limit the latency when loading large amounts of data. An animated visualization appearing quickly, unfolding, and getting more accurate with time, enables users to make early decisions. However, intermediate mean estimates are computed only on partial data and may not have time to converge to the true means, potentially misleading users and resulting in incorrect decisions. To address this issue, we propose two new designs visualizing the history of values in progressive bar charts, in addition to the use of confidence intervals. We comparatively study four progressive bar chart designs: with/without confidence intervals, and using near-history representation with/without confidence intervals, on three realistic data distributions. We evaluate user performance based on the percentage of correct answers (accuracy), response time, and user confidence. Our results show that, overall, users can make early and accurate decisions with 92% accuracy using only 18% of the data, regardless of the design. We find that our proposed bar chart design with only near-history is comparable to bar charts with only confidence intervals in performance, and the qualitative feedback we received indicates a preference for designs with history.

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization.

Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization.

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at.

KD-Box: Line-segment-based KD-tree for Interactive Exploration of Large-scale Time-Series Data.

Time-series data-usually presented in the form of lines-plays an important role in many domains such as finance, meteorology, health, and urban informatics. Yet, little has been done to support interactive exploration of large-scale time-series data, which requires a clutter-free visual representation with low-latency interactions. In this paper, we contribute a novel line-segment-based KD-tree method to enable interactive analysis of many time series. Our method enables not only fast queries over time series in selected regions of interest but also a line splatting method for efficient computation of the density field and selection of representative lines. Further, we develop KD-Box, an interactive system that provides rich interactions, e.g., timebox, attribute filtering, and coordinated multiple views. We demonstrate the effectiveness of KD-Box in supporting efficient line query and density field computation through a quantitative comparison and show its usefulness for interactive visual analysis on several real-world datasets.

On the Homogeneity of a Cobalt-Based Water Oxidation Catalyst.

The homogeneity of molecular Co-based water oxidation catalysts (WOCs) has been a subject of debate over the last 10 years as assumed various homogeneous Co-based WOCs were found to actually form CoO x under operating conditions. The homogeneity of the Co(HL) (HL = N,N-bis(2,2'-bipyrid-6-yl)amine) system was investigated with cyclic voltammetry, electrochemical quartz crystal microbalance, and X-ray photoelectron spectroscopy. The obtained experimental results were compared with heterogeneous CoO x . Although it is shown that Co(HL) interacts with the electrode during electrocatalysis, the formation of CoO x was not observed. Instead, a molecular deposit of Co(HL) was found to be formed on the electrode surface. This study shows that deposition of catalytic material is not necessarily linked to the decomposition of homogeneous cobalt-based water oxidation catalysts.

P-Terminated InP (001) Surfaces: Surface Band Bending and Reactivity to Water.

Stable InP (001) surfaces are characterized by fully occupied and empty surface states close to the bulk valence and conduction band edges, respectively. The present photoemission data show, however, a surface Fermi level pinning only slightly below the midgap energy which gives rise to an appreciable surface band bending. By means of density functional theory calculations, it is shown that this apparent discrepancy is due to surface defects that form at finite temperature. In particular, the desorption of hydrogen from metalorganic vapor phase epitaxy grown P-rich InP (001) surfaces exposes partially filled P dangling bonds that give rise to band gap states. These defects are investigated with respect to surface reactivity in contact with molecular water by low-temperature water adsorption experiments using photoemission spectroscopy and are compared to our computational results. Interestingly, these hydrogen-related gap states are robust with respect to water adsorption, provided that water does not dissociate. Because significant water dissociation is expected to occur at steps rather than terraces, surface band bending of a flat InP (001) surface is not affected by water exposure.

Exploring neighborhoods in large metagenome assembly graphs using spacegraphcats reveals hidden sequence diversity.

Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/spacegraphcats/spacegraphcats under the 3-Clause BSD License.

Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco.

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

Vega-Lite: A Grammar of Interactive Graphics.

We present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization. In contrast to existing reactive specifications, Vega-Lite selections decompose an interaction design into concise, enumerable semantic units. We evaluate Vega-Lite through a range of examples, demonstrating succinct specification of both customized interaction methods and common techniques such as panning, zooming, and linked selection.

Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations.

General visualization tools typically require manual specification of views: analysts must select data variables and then choose which transformations and visual encodings to apply. These decisions often involve both domain and visualization design expertise, and may impose a tedious specification process that impedes exploration. In this paper, we seek to complement manual chart construction with interactive navigation of a gallery of automatically-generated visualizations. We contribute Voyager, a mixed-initiative system that supports faceted browsing of recommended charts chosen according to statistical and perceptual measures. We describe Voyager's architecture, motivating design principles, and methods for generating and interacting with visualization recommendations. In a study comparing Voyager to a manual visualization specification tool, we find that Voyager facilitates exploration of previously unseen data and leads to increased data variable coverage. We then distill design implications for visualization tools, in particular the need to balance rapid exploration and targeted question-answering.