arXiv subjects

Kathleen Gregory

Publications and source records attributed to Kathleen Gregory.

15 recordsLinked to original sources

arXiv2026

Sustaining Dryad: Reconfiguring relationships and 'thinking like a business' to maintain open data infrastructures

Open data infrastructures underpin how open research is practiced across communities. While critical, the longevity of such infrastructures is far from certain, as they grapple with challenges related to technologies, dominant ideologies, and continued funding streams. This paper draws on a mixed-methods study of Dryad, a well-known open data infrastructure, to deeply explore one of these challenges: the quest for stable funding. Our analysis shows how Dryad has carefully reconfigured different forms of relationships and revenue models throughout its history to work towards financial sustainability. We identify four types of relationships with customers, collaborators, and competitors that have been critical to Dryad's financial evolution: reinforcing, forging, positioning, and excluding relationships. We argue that while implementing strategic, business thinking is a critical strategy, it is also one which shapes other factors important in sustainability: interpretations of value(s), community, and governance. We conclude by highlighting emerging tensions that provide insight for other open data infrastructures working to become financially sustainable. As a whole, our analysis focuses not just on financial mechanisms for funding open data infrastructures (although those emerge) but on the relationships which enable them.

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arXiv2026

Sustaining Knowledge Infrastructures: Asking the Right Questions and Listening for Answers

Sustaining knowledge infrastructures remains a persistent issue that requires continued engagement from diverse stakeholders as new questions and values arise in relation to KI maintenance. We draw on existing academic literature, practical experience with KI projects, and our discussions at a 2024 workshop for researchers and practitioners exploring KI evaluation to pose five questions for KI project managers to consider when thinking about how to make their KIs evolve sustainably over time. These questions include reflecting on sustainability throughout the life cycle of KIs, communicating evolving visions and values, engaging communities, right sizing a KI, and developing an iterative process for decision-making. Reflecting on these themes, we suggest, can support KI stakeholders to evolve, not necessarily grow, to meet the needs and values of their communities. How these themes are discussed will necessarily vary by funding sources, disciplines, governance, communities, and other contextual factors. However, adopting a deliberate and strategic approach to KI sustainability and aligning the invisible infrastructural work of KI maintenance with the outward-facing institutional work is, we argue, relevant to all KIs.

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arXiv2025

Encountering Friction, Understanding Crises: How Do Digital Natives Make Sense of Crisis Maps?

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.

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arXiv2025

The Gulf of Interpretation: From Chart to Message and Back Again

Charts are used to communicate data visually, but often, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we investigate how data journalists encode data and how members of a broad audience engage with, experience, and understand these visualizations. We conducted workshops and interviews with school and university students, job seekers, designers, and senior citizens to collect perceived messages and feedback on eight real-world charts. We analyzed these messages and compared them to the intended message. Our results help to understand the gulf that can exist between messages (that producers encode) and viewer interpretations. In particular, we find that consumers are often overwhelmed with the amount of data provided and are easily confused with terms that are not well known. Chart producers tend to follow strong conventions on how to visually encode particular information that might not always benefit consumers.

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arXiv2024

Human-Data Interaction: Thinking beyond individual datasets

Having greater access to data leads to many benefits, from advancing science to promoting accountability in government to boosting innovation. However, merely providing data access does not make data easy to use; even when data is openly available online, people may struggle to work with it. In this article, we draw on prior work, including our own, and a case study of Kaggle (a large online data science community) to discuss the importance of moving away from viewing datasets as static resources. Instead, we describe the view of data as a process with its own interactional affordances that offer many different possibilities for data, as well as for social interaction. We advocate for the notion of Human Data Interactions and their potential implications for various audiences.

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arXiv2024

Data journeys in popular science: Producing climate change and COVID-19 data visualizations at Scientific American

Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics. However, visualizations often conceal the many contexts involved in their production, ranging from decisions made in research labs about collecting and sharing data to choices made in editorial rooms about which data stories to tell. In this paper, we examine how data visualizations about climate change and COVID-19 are produced in popular science magazines, using Scientific American, an established English-language popular science magazine, as a case study. To do this, we apply the analytical concept of data journeys (Leonelli, 2020) in a mixed methods study that centers on interviews with Scientific American staff and is supplemented by a visualization analysis of selected charts. In particular, we discuss the affordances of working with open data, the role of collaborative data practices, and how the magazine works to counter misinformation and increase transparency. This work provides an empirical contribution by providing insight into the data (visualization) practices of science communicators and demonstrating how the concept of data journeys can be used as an analytical framework.

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arXiv2024

"Being Simple on Complex Issues" -- Accounts on Visual Data Communication about Climate Change

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.

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arXiv2023

Practitioners' Perspectives on Designing Data Visualizations for the General Public

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.

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arXiv2023

"The main message is that sustainability would help" -- Reflections on takeaway messages of climate change data visualizations

How do different audiences make sense of climate change data visualizations and what do they take away as a main message? To investigate this question, we are building on the results of a previous study, focusing on expert opinions regarding public climate change communication and the role of data visualizations. Hereby, we conducted semi-structured interviews with 17 experts in the fields of climate change, science communication, or data visualization. We also interviewed six lay persons with no professional background in either of these areas. With this analysis, we aim to shed light on how lay audiences arrive at an understanding of climate change data visualizations and what they take away as a main message. For two exemplary data visualizations, we compare their takeaway messages with messages formulated by experts. 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.

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arXiv2023

What is the message? Perspectives on Visual Data Communication

Data visualizations are widely used to communicate messages about urgent topics such as climate change and public health. However, we still know little about how these visualizations are produced and interpreted in popular science contexts. In this mixed-method study, we examine how data are visually communicated and understood in the popular science magazine Scientific American, focusing on the messages these visualizations convey. To capture this complexity, we analyze data visualizations about climate change and pandemics in Scientific American over the past fifty years from three complementary perspectives: reader, chart, and producer. From the reader's perspective, we articulate takeaway messages and document sensemaking, interpreting visualizations first without and then with textual elements. From the chart perspective, we examine how visual features and text shape interpretation. From the producer's perspective, we draw on interviews with Scientific American staff to understand message planning and compare a sample of their intended messages with those we interpreted. Using takeaway messages as our central analytic lens, we develop a message typology and show that messages vary systematically across dimensions such as granularity, articulation, and inference. A key finding is that text plays a pivotal role: approximately two-thirds of messages change when textual elements are added. While the interviews highlighted the central role of message planning in visualization production, intended and interpreted messages only partially aligned. Our findings underscore the importance of contextual clarity and audience-aware communication, and we derive recommendations for visualization designers and science communicators.

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arXiv2023

Supporting data discovery: A meta-synthesis comparing perspectives of support specialists and researchers

Purpose: Data discovery practices currently tend to be studied from the perspective of researchers or the perspective of support specialists. This separation is problematic, as it becomes easy for support specialists to build infrastructures and services based on perceptions of researchers' practices, rather than the practices themselves. This paper brings together and analyzes both perspectives to support the building of effective infrastructures and services for data discovery. Methods: This is a meta-synthesis of work the authors have conducted over the last six years investigating the data discovery practices of researchers from different disciplines, with a focus on the social sciences, and support specialists. We bring together and re-analyze data collected from in-depth interview studies with 6 support specialists in the field of social science in Germany, with 21 social scientists in Singapore, an interview with 10 researchers and 3 support specialists from multiple disciplines, a global survey with 1630 researchers and 47 support specialists from multiple disciplines, an observational study with 12 researchers from the field of social science and a use case analysis of 25 support specialists from multiple disciplines. Results: We found that there are many similarities in what researchers and support specialists want and think about data discovery, both in social sciences and in other disciplines. There are, however, some differences which we have identified, most notably the interconnection of data discovery with web search, literature search and social networks. Conclusion: We conclude by proposing recommendations for how different types of support work can address these points of difference to better support researchers' data discovery practices.

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arXiv2020

Talking datasets: Understanding data sensemaking behaviours

The sharing and reuse of data are seen as critical to solving the most complex problems of today. Despite this potential, relatively little is known about a key step in data reuse: people's 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 as they summarised and interacted with both familiar and unfamiliar data. We use our findings to identify and detail common activity patterns and necessary data attributes across three clusters of sensemaking activities: inspecting data, engaging with content, and placing data within broader contexts. We conclude by proposing design recommendations for tools and documentation practices which can be used to facilitate sensemaking and subsequent data reuse.

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arXiv2020

Lost or found? Discovering data needed for research

Finding data is a necessary precursor to being able to reuse data, although relatively little large-scale empirical evidence exists about how researchers discover, make sense of and (re)use data for research. This study presents evidence from the largest known survey investigating how researchers discover and use data that they do not create themselves. We examine the data needs and discovery strategies of respondents, propose a typology for data reuse and probe the role of social interactions and literature search in data discovery. We consider how data communities can be conceptualized according to data uses and propose practical applications of our findings for designers of data discovery systems and repositories. Specifically, we consider how to design for a diversity of practices, how communities of use can serve as an entry point for design and the role of metadata in supporting both sensemaking and social interactions.

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arXiv2020

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours 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. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

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arXiv2019

Understanding Data Search as a Socio-technical Practice

Open research data are heralded as having the potential to increase effectiveness, productivity, and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behavior and ultimately help to improve the design of data discovery systems.

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