Conceptual data modeling is a central activity in data work, yet how such models are created remains understudied. While data attributes play a key role, modeling is also shaped by tasks, tools, developers’ prior experiences, and often unfolds collaboratively between diverse stakeholders. In this study, we invited 22 participants with varying expertise in pairs to collaboratively sketch conceptual data models. We captured screen recordings, their evolving sketches, and conversations. Through a mixed-methods approach combining thematic analysis of dialogue with an examination of model artifacts, we identify how communication and collaboration patterns influenced the process. Our findings reveal a range of collaborative strategies and representations, as well as distinct ways dialogue shaped the emergence and expression of shared conceptual models. These insights deepen understanding of Human-Data Interaction in collaborative data work and point to design opportunities for tools that better support communication, negotiation, and sensemaking of data.

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.

Contemporary discourse on data communication has discussed logos (reason) and, more recently, ethos (credibility) extensively. While the concept of pathos (emotional appeal) has received growing attention in the visualization community in recent years, its connection to related concepts such as rhetoric and aesthetics remains underexplored. In this paper, we provide working definitions of these terms, contextualize them within data visualization, and explore their overlaps and differences in light of their historical development. This historical perspective offers a more holistic understanding of how these approaches in science and philosophy have evolved over time, contributing to a deeper comprehension of their integration into the design process. Drawing on Campbell's seven circumstances, we illustrate how pathos functions as a rhetorical strategy in contemporary data visualizations, examining the interplay of rhetorical strategies, aesthetic qualities, and offering our interpretation of how these elements work together.

How audiences read, interpret, and critique data visualizations is mainly assessed through performance tests featuring tasks like value retrieval. Yet, other factors shown to shape visualization understanding, such as numeracy, graph familiarity, and aesthetic perception, remain underrepresented in existing instruments. To address this, we design and test a Multidimensional Assessment Method for Visualization Understanding (MdamV). This method integrates task-based measures with self-perceived ability ratings and open-ended critique, applied directly to the visualizations being read. Grounded in learning sciences frameworks that view understanding as a multifaceted process, MdamV spans six dimensions: Comprehending, Decoding, Aestheticizing, Critiquing, Reading, and Contextualizing. Validation was supported by a survey (N = 438) representative of Austria's population (ages 18-74, male/female split), using a line chart and a bar chart on climate data. Findings show, for example, that about a quarter of respondents indicate deficits in comprehending simple data units, roughly one in five people felt unfamiliar with each chart type, and self-assessed numeracy was significantly related to data reading performance (p = 0.0004). Overall, the evaluation of MdamV demonstrates the value of assessing visualization understanding beyond performance, framing it as a situated process tied to particular visualizations.

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.

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the “XAI Novice Question Bank”, an extension of the XAI Question Bank (Liao et al., 2020) containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences. • People affected by algorithmic systems should be better considered in explainable AI. • Their interests lie in a system’s context and usage rather than in technical details. • Explanations must meet their information needs in order to support their agency. • Leveraging cognitive processes could improve the understandability of explanations. • Affected people’s perceptions of risks and benefits impact their information needs.

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.

Video summaries of scientific publications have gained more and more popularity over the last years, requiring many researchers to familiarize themselves with the tools and techniques of video production which can be an overwhelming task. This paper introduces a video structuring framework embedded into the authoring tool Pub2Vid. The tool supports users with the creation of their video outline and script, providing real video examples and recommendations based on the analysis of 40 publication summarization videos which were rated in a user study with 68 participants. Following a four-tier evaluation methodology, the application’s usability is assessed and improved via amateur and expert interviews, two rounds of usability tests and two case studies. It is shown that the tool and its recommendations are particularly useful for beginners due to the simple design and intuitive components as well as suggestions based on real video examples.

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.

Data stories are about communicating data, tailored to a specific audience, with a compelling narrative. Creating them requires a mix of data science and design skills, which can be difficult for beginners. Patterns can help, as they provide tried-and-tested solutions to commonly occurring challenges. 'Narrative patterns' are a particular class of patterns that support data-storytellers in structuring the presentation of data within their story, aiding them in effectively communicating with their audience. Our aim is to understand how such patterns are applied in practice and identify ways they could be of greater use, especially for people new to the field. To this end, we conduct a review of 67 data stories, created by both professional data storytellers and by postgraduate university students studying data-science, to analyse their use of narrative patterns. Starting from a collection of narrative patterns from the literature, we explore which patterns are used more often, either on their own or in combination, and which ones beginners struggle with. From the findings we derive recommendations on how to refine some of the less accessible patterns and for training and tool support, which would allow wider audiences to articulate their data insights effectively.

Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries. In this paper, we explore the characteristics of text summaries of data in order to understand how meaningful summaries look like. We present two complementary studies: a data-search diary study with 69 students, which offers insight into the information needs of people searching for data; and a summarisation study, with a lab and a crowdsourcing component with overall 80 data-literate participants, who produced summaries for 25 datasets. In each study we carried out a qualitative analysis to identify key themes and commonly mentioned dataset attributes, which people consider when searching and making sense of data. The results helped us design a template to create more meaningful textual representations of data, alongside guidelines for improving data-search experience overall.

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta-released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems and discuss what makes dataset search a field in its own right, with unique challenges and open questions. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to tackle these questions as well as immediate next steps that will take the field forward.

Collaborative work with data is increasingly common and spans a broad range of activities - from creating or analysing data in a team, to sharing it with others, to reusing someone else's data in a new context. In this paper, we explore collaboration practices around structured data and how they are supported by current technology. We present the results of an interview study with twenty data practitioners, from which we derive four high-level user needs for tool support. We compare them against the capabilities of twenty systems that are commonly associated with data activities, including data publishing software, wikis, web-based collaboration tools, and online community platforms. Our findings suggest that data-centric collaborative work would benefit from: structured documentation of data and its lifecycle; advanced affordances for conversations among collaborators; better change control; and custom data access. The findings help us formalise practices around data teamwork, and build a better understanding how people's motivations and barriers when working with structured data.

This half day workshop explores challenges in data search, with a particular focus on data on the web. We want to stimulate an interdisciplinary discussion around how to improve the description, discovery, ranking and presentation of structured and semi-structured data, across data formats and domain applications. We welcome contributions describing algorithms and systems, as well as frameworks and studies in human data interaction. The workshop aims to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly.

Structured data such as databases, spreadsheets and web tables is becoming critical in every domain and professional role. Yet we still do not know much about how people interact with it. Our research focuses on the information seeking behaviour of people looking for new sources of structured data online, including the task context in which the data will be used, data search, and the identification of relevant datasets from a set of possible candidates. We present a mixed-methods study covering in-depth interviews with 20 participants with various professional backgrounds, supported by the analysis of search logs of a large data portal. Based on this study, we propose a framework for human structured-data interaction and discuss challenges people encounter when trying to find and assess data that helps their daily work. We provide design recommendations for data publishers and developers of online data platforms such as data catalogs and marketplaces. These recommendations highlight important questions for HCI research to improve how people engage and make use of this incredibly useful online resource.