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https://www.selleckchem.com/products/abemaciclib.html Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language.We compare physical and virtual reality (VR) versions of simple data visualizations. We also explore how the addition of virtual annotation and filtering tools affects how viewers solve basic data analysis tasks. We report on two studies, inspired by previous examinations of data physicalizations. The first study examined differences in how viewers interact with physical hand-scale, virtual hand-scale,and virtual table-scale visualizations and the impact that the different forms had on viewer's problem solving behavior. A second study examined how interactive annotation and filtering tools might sup-port new modes of use that transce

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