We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

OBJECTIVES: Data visualization style guides are standards for formatting and designing representations of information, like charts, graphs, tables, and diagrams. To assist researchers communicate their visual content in better and more effective ways, this article accomplishes two tasks. First, we take a detailed look at a data visualization style guide and its components-what it is and what it should include. Second, we create a detailed template for the color section of a data visualization style guide. TARGET AUDIENCE: Creating a data visualization style guide as described here should help researchers across multiple disciplines create better and more consistent charts, graphs, and diagrams. Such style guides are useful for individuals and organizations in their efforts to be more efficient and consistent in their data communication products. SCOPE: Data visualization style guides often include explaining the what (eg, types of charts), the why (eg, reasons for using specific colors), and the how (eg, tools or templates) of creating consistent and effective visuals that can also fit within an individual's or organization's larger design system. We use a variety of tools to create, test, and implement a data visualization color palette. We provide sample color palettes and provide step-by-step instructions on how to import those palettes into six popular data visualization tools: Microsoft Excel and PowerPoint, Tableau, PowerBI, and Adobe Illustrator and InDesign.