Public-facing data visualizations can play a vital role in making complex information clear and engaging, thereby encouraging informed public discourse and participation. However, existing work offers limited insight into how practitioners make design decisions based on their envisioned target audiences and across different media channels. To investigate this, we conducted semi-structured interviews with 21 professionals from journalistic settings, focusing on how they conceptualize their readers, translate these notions into design choices, and evaluate their work. We found that practitioners often rely on broad audience definitions, despite considering “knowing their readers” essential. Evaluation primarily relies on peer feedback or social metrics rather than user testing. From these accounts, we identify recurring strategies employed to reach general, often undefined publics. We discuss implications for audience-centered authoring tools, proposing features such as persona simulations and content-adaptive multi-format authoring, message-first rhetoric-aware workflows, and lightweight in-tool evaluation to better support the realities of public-facing design.

Crisis maps are regarded as crucial tools in crisis communication, as demonstrated during the COVID-19 pandemic and climate change crises. However, there is limited understanding of how public audiences engage with these maps and extract essential information. Our study investigates the sensemaking of young, digitally native viewers as they interact with crisis maps. We integrate frameworks from the learning sciences and human-data interaction to explore sensemaking through two empirical studies: a thematic analysis of online comments from a New York Times series on graph comprehension, and interviews with 18 participants from German-speaking regions. Our analysis categorizes sensemaking activities into established clusters: inspecting, engaging with content, and placing, and introduces responding personally to capture the affective dimension. We identify friction points connected to these clusters, including struggles with color concepts, responses to missing context, lack of personal connection, and distrust, offering insights for improving crisis communication to public audiences.

Data visualizations play a critical role in both communicating scientific evidence about climate change and in stimulating engagement and action. To investigate how visualizations can be better utilized to communicate the complexities of climate change to different audiences, we conducted interviews with 17 experts in the fields of climate change, data visualization, and science communication, as well as with 12 laypersons. Besides questions about climate change communication and various aspects of data visualizations, we also asked participants to share what they think is the main takeaway message for two exemplary climate change data visualizations. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups. On average, experts formulated shorter and more abstract messages, often referring to higher-level conclusions rather than specific details. We use our findings to reflect on design decisions for creating more effective visualizations, particularly in news media sources geared toward lay audiences. We hereby discuss the adaption of contents according to the needs of the audience, the trade-off between simplification and accuracy, as well as techniques to make a visualization attractive.

The sharing and reuse of data are seen as critical to solving the most complex problems of today. Despite this potential, relatively little attention has been paid to a key step in data reuse: the behaviours involved in data-centric sensemaking. We aim to address this gap by presenting a mixed-methods study combining in-depth interviews, a think-aloud task and a screen recording analysis with 31 researchers from different disciplines as they summarised and interacted with both familiar and unfamiliar data. We use our findings to identify and detail common patterns of data-centric sensemaking across three clusters of activities that we present as a framework: inspecting data, engaging with content, and placing data within broader contexts. Additionally, we propose design recommendations for tools and documentation practices, which can be used to facilitate sensemaking and subsequent data reuse.

A cross-disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

Over the past decades, science has experienced rapid growth in the volume of data available for research—from a relative paucity of data in many areas to what has been recently described as a data deluge [‎1]. Data volumes have increased exponentially across all fields of science and human endeavour, including data from sky, earth, and ocean observatories; social media such as Facebook and Twitter; wearable health-monitoring devices; gene sequences and protein structures; and climate simulations [‎2]. This brings opportunities to enable more research, especially cross-disciplinary research that could not be done before. However, it also introduces challenges in managing, describing, and making data findable, accessible, interoperable, and reusable by researchers [‎3]. When this vast amount and variety of data is made available, finding relevant data to meet a research need is increasingly a challenge. In the past, when data were relatively sparse, researchers discovered existing data by searching literature, attending conferences, and asking colleagues. In today’s data-rich environment, with accompanying advances in computational and networking technologies, researchers increasingly conduct web searches to find research data. The success of such searches varies greatly and depends to a large degree on the expertise of the person looking for data, the tools used, and, partially, on luck. This article offers the following 11 quick tips that researchers can follow to more effectively and precisely discover data that meet their specific needs.