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reVISit is an open-source software toolkit and framework for creating, deploying, and monitoring empirical visualization studies. Running a quality empirical study in visualization can be demanding and resource-intensive, requiring substantial time, cost, and technical expertise from the research team. These challenges are amplified as research norms trend towards more complex and rigorous study methodologies, alongside a growing need to evaluate more complex interactive visualizations. reVISit aims to ameliorate these challenges by introducing a domain-specific language for study set-up, and a series of software components, such as UI elements, behavior provenance, and an experiment monitoring and management interface. Together with interactive or static stimuli provided by the experimenter, these are compiled to a ready-to-deploy web-based experiment. We demonstrate reVISit's functionality by re-implementing two studies --- a graphical perception task and a more complex, interactive study. reVISit is an open-source community project, available at https://revisit.dev/. 

Provenance tracking is widely acknowledged as an important component of visualization systems. By tracking provenance data, visualization designers can achieve a wide variety of important functionality, ranging from action recovery (undo/redo), reproducibility, collaboration and sharing, to logging in support of quantitative and longitudinal evaluation. Yet, for web-based visualizations, there are currently no libraries that make provenance tracking easy to implement in visualization systems. The result of this is that visualization designers either develop ad-hoc solutions that are rarely comprehensive, or don't track provenance at all. In this paper, we introduce a web-based software library --- Trrack --- that is designed for easy integration in existing or future visualization systems. Trrack supports a wide range of use cases, from simple action recovery, to capturing intent and reasoning, and can be used to share states with collaborators and store provenance on a server. Trrack also includes an optional provenance visualization component that supports annotation of states and aggregation of events. 

Citrus yellow-vein disease (CYVD) was first reported in California in 1957. We now report that CYVD is associated with a virus-like agent, provisionally named citrus yellow-vein associated virus (CYVaV). The CYVaV RNA genome has 2,692 nucleotides and codes for two discernable open reading frames (ORFs). ORF1 encodes a protein of 190 amino acid (aa) whereas ORF2 is presumably generated by a −1 ribosomal frameshifting event just upstream of the ORF1 termination signal. The frameshift product (717 aa) encodes the RNA-dependent RNA polymerase (RdRp). Phylogenetic analyses suggest that CYVaV is closely related to unclassified virus-like RNAs in the family Tombusviridae . Bio-indexing and RNA-seq experiments indicate that CYVaV can induce yellow vein symptoms independently of known citrus viruses or viroids. 

Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.