Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations.

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely "cross-type collaborations", are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at https://32vis.hongtaoh.com and https://osf.io/zkvjm.

Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation.

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one's belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process. Through two crowdsourced experiments, we demonstrate that supposedly objective assessments of the strength of a correlational relationship can be influenced by how strongly a viewer believes in the existence of that relationship. Participants viewed scatterplots depicting a relationship between meaningful variable pairs (e.g., number of environmental regulations and air quality) and estimated their correlations. They also estimated the correlation of the same scatterplots labeled instead with generic 'X' and 'Y' axes. In a separate section, they also reported how strongly they believed there to be a correlation between the meaningful variable pairs. Participants estimated correlations more accurately when they viewed scatterplots labeled with generic axes compared to scatterplots labeled with meaningful variable pairs. Furthermore, when viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1. While data visualizations are typically thought to present objective truths to the viewer, these results suggest that existing personal beliefs can bias even objective statistical values people extract from data.

VisHikers' Guide to Evaluation: Competing Considerations in Study Design.

In this Viewpoint article, we describe the persistent tensions between various camps on the "right" way to conduct evaluations in visualization. Visualization as a field is the amalgamation of cognitive and perceptual sciences and computer graphics, among others. As a result, the relatively disjointed lineages in visualization understandably approach the topic of evaluation very differently. It is both a blessing and a curse to our field. It is a blessing, because the collaboration of diverse perspectives is the breeding ground of innovation. Yet it is a curse, because as a community, we have yet to resolve an appreciation for differing perspectives on the topic of evaluation. We explicate these differing expectations and conventions to appreciate the spectrum of evaluation design decisions. We describe some guiding questions that researchers may consider when designing evaluations to navigate differing readers' evaluation expectations.

Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments.

Alternative text is critical in communicating graphics to people who are blind or have low vision. Especially for graphics that contain rich information, such as visualizations, poorly written or an absence of alternative texts can worsen the information access inequality for people with visual impairments. In this work, we consolidate existing guidelines and survey current practices to inspect to what extent current practices and recommendations are aligned. Then, to gain more insight into what people want in visualization alternative texts, we interviewed 22 people with visual impairments regarding their experience with visualizations and their information needs in alternative texts. The study findings suggest that participants actively try to construct an image of visualizations in their head while listening to alternative texts and wish to carry out visualization tasks (e.g., retrieve specific values) as sighted viewers would. The study also provides ample support for the need to reference the underlying data instead of visual elements to reduce users' cognitive burden. Informed by the study, we provide a set of recommendations to compose an informative alternative text.

Bayesian-Assisted Inference from Visualized Data.

A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and subjective uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication.

Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty.

People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively.

Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation.

In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people's perspectives on the data. However, how such social information embedded in a visualization impacts a viewer's interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people's expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people's expectations on people's ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people's expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others' expectations alongside data.

Sniffing for gene-silencing efficiency of siRNAs in HeLa cells in comparison with that in HEK293T cells: correlation between knockdown efficiency and sustainability of sirnas revealed by FRET-based probing.

Investigation of the intracellular fate of small interfering RNAs (siRNAs) following their delivery into cells is of great importance to elucidate their dynamics in cytoplasm. Here we describe the use of an advanced fluorescence-based method to probe the dissociation and/or degradation of double-labeled siRNAs in HeLa cells in comparison with that in human embryonic kidney 293T (HEK293T) cells. This work was performed with three siRNAs labeled with fluorescence resonance energy transfer (FRET) dyes, allowing a non-destructive and non-invasive assessment of the dissociation and degradation state of siRNAs in cultured cells. Our FRET analysis not only shows the asymmetric degradation as well as the time-dependent dissociation of each siRNA strand during the measured time period, underlining the high intrinsic nuclease resistance of duplex siRNAs, but also reveals the longer sustainability of siRNAs in HeLa cells compared with that in HEK293T cells, explaining the gene silencing in HeLa cells is more efficient than that in HEK293T cells. In addition, our single-molecule FRET assays demonstrate the potential of the delineated fluorescence-based technique for future research on biological behavior of siRNAs even at the single-molecule level. The fluorescence-based method is a straightforward technique to gain direct information on siRNA integrity inside living cells, which can provide a detection tool for dynamics of biological molecules.

Stepwise fluorescence changes of quantum dots: single-molecule spectroscopic studies on the properties of turn-on quantum dots.

Single-molecule spectroscopy of turn-on quantum dots induced by NADPH-dependent biocatalyzed transformations reveals that the fluorescence intensities of quantum dots functionalized with Nile Blue are stepwisely and reversibly changed in the presence of NADPH.

Detection of single-base mutation in RNA using T4 RNA ligase-based nick-joining or DNAzyme-based nick-generation.

A single nucleotide polymorphism (SNP) is a common genetic variation when a single nucleotide differs between members of a species or paired chromosome. Due to its association with disease susceptibility and drug resistance, SNP detection is of great value in studying the variation in drug responses. Here we present two quantitative SNP detection methods for a single-base mismatch in RNA, based on nick-joining and nick-generating activities of T4 RNA ligase and DNAzyme, respectively. T4 RNA ligase successfully discriminated a one-base mismatch in the ligation junction, and the designed DNAzyme cleaved RNA by discerning a single-base mismatch in the cleaving site.

Functionalized quantum dots to quantify NADPH and their use for NADP+-dependent biocatalyzed transformations.

Quantum dots are semiconducting nanoparticles that can be prepared with interesting optical properties. The fluorescent properties of quantum dots are one of the key advantages for their use as optical labels for biorecognition events and biocatalytic processes. We have prepared semiconductor quantum dots conjugated with Nile Blue (NB), and demonstrate that NB-functionalized quantum dots can act as versatile probes to analyze different biocatalyzed transformations, and can be used for the quantitative detection of NADPH as well as NADH. This approach provides a new path for the optical detection of NAD(P)H and for the quantitative analysis of NAD(P)(+)-dependent biotransformations.