The digitalisation of our societies has made data ubiquitous, capturing the behaviors of individuals through their interactions with products, services, and systems. As design and HCI researchers increasingly integrate data throughout human-centered and participatory design processes, the highly dynamic nature of behavioral data reveals its deep interconnection with people, their behaviors, and experiences. Data-Centric Design leverages data as material for subjective inquiry–an entry point to better understand human dynamics through deeper reflection in research and design processes. This Research Topic aims to collect emerging, illustrative projects from the community, share the messiness of research and design processes with data, and surface good practices for effective and responsible data use. This need emerged from the guest editors' engagement in a series of Special Interest Group (SIG) meet-ups (Gomez Ortega et al., 2023) and workshops (Lee-Smith et al., 2023) at the ACM conference on Human Factors in Computing Systems (CHI). [...]

Designers often engage with video to gain rich, temporal insights about the context of users, collaboratively analyzing it to gather ideas, challenge assumptions, and foster empathy. To capture the full visual context of users and their situations, designers are adopting 360° video, providing richer, more multi-layered insights. Unfortunately, the spherical nature of 360° video means designers cannot create tangible video artifacts such as storyboards for collaborative analysis. To overcome this limitation, we created Tangi, a web-based tool that converts 360° images into tangible 360° video artifacts, that enable designers to embody and share their insights. Our evaluation with nine experienced designers demonstrates that the artifacts Tangi creates enable tangible interactions found in collaborative workshops and introduce two new capabilities: spatial orientation within 360° environments and linking specific details to the broader 360° context. Since Tangi is an open-source tool, designers can immediately leverage 360° video in collaborative workshops.

This paper presents a conceptual exploration of designing sexual pleasure as an evolving whole-body experience. It addresses the historically narrow focus of research and technology on functional outcomes such as reproduction and orgasm. This limited perspective overlooks diverse desires, emotional connection, and sensory engagement, reinforcing restrictive norms that shape how individuals conceptualise and experience sexuality. To inform our design inquiry, we conducted a qualitative survey (N=143) to generate how individuals understand and experience sexual pleasure. Reflexive thematic analysis of the responses reveals the influence of culture and technology on sexuality, alongside several experiential dimensions: emotional and embodied connection, play and sensory immersion, and vulnerability. These insights, together with a theoretical foundation, guide a design exploration communicated through two provocations. These provocations serve as reflections of an alternative design orientation; one that challenges normative assumptions, views pleasure as an ongoing process, supports bodily exploration, and facilitates richer, more inclusive sexual experiences.

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration - characterized by dyadic comparison and structured roles for questioning and reasoning - has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, PAIRcolator. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.

Cancer treatments often lead to sexual health challenges that greatly impact cancer survivors’ quality of life. Current interventions primarily address physiological aspects, like medication or vaginal care, overlooking psychological, social, and cultural dimensions. This paper explores how HCI can address this gap by supporting post-cancer sexual health with interventions for survivors and their partners, considering their lived experiences. Through reflexive thematic analysis of interviews with (N=6) medical sexologists, we identified five themes: perceiving the body as a medical object, the hot potato problem in oncology, sociotechnical sexploration, reuniting what treatment has divided, and designing interventions with openness in a highly situated context. These themes highlight cancer survivors’ experiences, the (in)effectiveness of current interventions, and provision of care. This research outlines the design space for post-cancer sexual health by providing specific design directions (“what”) and ways for designing them (“how”), while advancing the broader discourse on intimacy and design within HCI.

The KEM category Data for Inquiry and Evidence encompasses a collection of methods and tools for generating insights from data, complementing other qualitative and quantitative design and design research methods. Thereby, it bridges the gap between Human-Centred Design and Data Science, emphasising the importance of individual context and the stories behind the data. Data for Inquiry and Evidence includes methodologies for utilising data in the design process as well as in the resulting designed products, systems, or services. As a next step these methodologies may be used for generating, analysing or validating data to support mission-driven innovation.

Remote Patient Management systems (RPM) are crucial for addressing healthcare workforce shortages. These systems are often designed with a specified focus on clinical functionalities, without proper consideration for human-centric concerns. A care perspective is essential not only to acknowledge patients as people, but also to foster better quality of care and, ultimately, adoption. This highlights the gap of how RPM can embed caring. This work offers a systematic literature review aimed at developing "Caring RPM", a normative framework that integrates the philosophy of caring from nursing theory into RPMs. This framework underwrites the practical, moral, and relational aspects of patient care, including actionable recommendations to recalibrate RPM systems for more effective human-centric design. The framework can inspire new ways of embedding the caring dimension into HCI design practices.

The design of collaborative personal informatics (PI) has shifted its focus from using one’s own data to integrating others’ data to enhance self-understanding. In this trend, understanding the effectiveness of the two data sources in facilitating personal insights becomes essential, as a comprehensive understanding of self-understanding requires insights from both individual and interpersonal perspectives. While recent studies have suggested the potential role of others’ data as a reflective medium to generate personal insights, little is understood about its distinctive effectiveness in personal insights generated compared to one’s own data. To address this gap, we conducted a crowdsourced study involving two participant groups (N1=N2=60) in a data-informed reflection task: Data Providers (DP) reflecting on their own data; Non-Data Providers (NDP) reflecting on the data provided by DP. Analyzing the textual responses, we assess the reflection levels, self-disclosure levels, and characteristics of personal insights. Our findings uncover that others’ data possess a comparable effectiveness in facilitating reflection and self-disclosure of personal thoughts and feelings. Others’ data displays a strength in supporting value judgments, while one’s own data excels in enhancing behavioral awareness. This research sheds light on the design of collaborative PI, offering insights into how to leverage the benefits while mitigating the disadvantages of both data sources to enhance the self-understanding.

Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.

Most people are entangled with an ever-growing trail of data that results from their daily interactions with products and services. Yet, they are hardly aware of the nature and characteristics of the data within this trail. We design dataslip, a provocative artifact that materializes the personal data trail into a receipt and aims to elicit creepiness. We demonstrate dataslip at two events in Delft, The Netherlands. Dataslip is a starting point to foster conversations with local community members about the underlying challenges and potential alternatives to personal data collection and use. We use these as prompts for further speculation through a collaborative futuring exercise with children, where we part from challenges towards hopeful and empowering futures. We contribute with an artifact that invites individuals to interrogate the current personal data practices they are embedded in and a set of five speculative design scenarios that suggest hopeful and empowering alternatives.

The increased ubiquity of 360° video presents a unique opportunity for designers to deeply engage with the world of users by capturing the complete visual context. However, the opportunities and challenges 360° video introduces for video design ethnography are unclear. This study investigates this gap through 16 workshops in which experienced designers engaged with 360° video. Our analysis shows that while 360° video enhances designers’ ability to explore and understand user contexts, it also complicates the process of sharing insights. To address this challenge, we present two opportunities to support the use of 360° video by designers - the creation of designerly 360° video annotation tools, and 360° “screenshots” - in order to enable designers to leverage the complete context of 360° video for user research.

Data donation is an emerging practice for collecting personal data. However, recent data donation approaches are insufficient in intimate research contexts as they perceive data as neutral and objective and do not consider the contexts where data is generated and shaped nor offer choices beyond whether to disclose data. In this paper, we investigate how Data Feminism can inform an alternative form of data donation and propose the Sensitive Data Donation (sDD) method. It recognizes the sensitive nature of data and assumes the importance of situating and contextualizing it through balanced participation from donors, either as contributors, collaborators, or co-creators. To develop the method, we conduct a scoping literature review where we conceptualize data donation theories and practices. These serve as a base to critique recent approaches and propose an alternative: sDD. It comprises five principles integrated into a five-phase approach. We conclude by discussing its limitations and future challenges.

Data donation is an emerging practice enabling personal data collection for research. While it offers opportunities to access new insights into people’s behavior and experiences through their digital-trace data, the role of individuals – as research participants – is limited in most data donation projects. They primarily contribute with data, limiting the perspectives included and accounted for around critical research-design decisions. In this paper, we explore the opportunity to embed data donation in research processes that are not only contributory but collaborative and co-created. To do so, we present a participatory data donation case study focused on athletes’ perceptions of the impact of their menstrual cycle on their sports performance through their physical activity data. Based on the data donation experiences of 20 athletes, our paper provides insights into people’s preferences and expectations in participatory data donation processes and discusses considerations for supporting various degrees of participation in future data donation research.

As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.

Recently, methods and approaches such as Participatory Data Analysis, Data-Enabled Design, and Contextual Inquiry have highlighted how design activities can benefit from behavioral data. This data offers new ways to learn from what people do and how they do it, across time and space. However, behavioral data introduces changes and frictions to design activities and poses several challenges for designers to overcome. In this paper, we conduct two workshops with 18 expert designers, from industry and academia, to understand the nature of these challenges, beyond the technical aspects. We contribute by underlining the challenges and opportunities of incorporating behavioral data into design activities; including a design perspective on data, interacting with participants, and interacting with regulatory bodies. We translate our findings into opportunities for a better alignment between regulatory bodies, designers, and participants. We propose to harness the iterative nature of design activities and embedded it into a process that allows for continuous reflection, reassessment, and review of highly dynamic datasets.

Behavioral data is ubiquitous in products, services, and systems that people interact with. It is increasingly used by design and HCI researchers and practitioners throughout their human-centered and participatory design processes. The highly dynamic nature of behavioral data makes it deeply intertwined with people, their behavior, and their experiences. Thus, it presents unique opportunities and challenges. This Special Interest Group will provide a space to reflect and discuss effective and responsible ways to engage with behavioral data in human-centered design processes. We will explore questions about the types and scale of data used, the contexts in which data is embedded and applied, the methods we rely on, and the forms of engagement of the multiple stakeholders. In doing so, our goal is to collaboratively develop a research agenda, setting the scope for an annual, international symposium on Data-Centric Design.

The digitization of services and global lock-downs have led an explosion of delivery services, which use gig-workers as delivery personnel. They can face apathy from both their employers and users of the service. Previous studies focused on mediating interactions between workers or workers and tasks. However, delivery presents the opportunity for HCI interventions to mediate the interaction between worker and users to increase their empathy. We conducted an empirical study where 63 participants ordered a drink with an app which presented a different level of information about the delivery person (nothing; name and photo; heart rate). Initial results show no significant impact on empathy measures between conditions, however post-hoc analysis showed that heart rate lead to increased Compassionate and decreased Affective empathy. This raises the question of what "type"of empathy is beneficial for delivery personnel and the need to refine the concept and measures of empathy used in HCI.

As we navigate the physical and digital world, we unknowingly leave behind an immense trail of data. We are informed about this via lengthy documents (e.g., privacy policies) or short statements (e.g., cookie popups). However, even when we know that data is collected, we remain largely unaware of its nature; what information it contains and how it relates to us. Data is highly personal. It contains and reveals information about our behavior and experiences scattered over time, which can be abstract and opaque even to us. Dataslip is an interactive installation where the construct of personal data is translated into a material and tangible representation in the form of a receipt or ‘personal data slip’. The receipt contains detailed information and illustrative examples of the data generated from our interactions with five different categories of products and services: (1) personalized public transport cards, (2) supermarket loyalty cards, (3) credit and debit cards, (4) wearables, and (5) mobile apps. Its length is proportional to the amount of data collected about us. With dataslip, we aim to reduce the distance between individuals and their personal data, elicit confrontation and invite people to question their role within the personal data ecosystems in which they are embedded.

Digital technologies have increasingly integrated into people's lives, continuously capturing their behavior through potentially sensitive data. In the context of voice assistants, there is a misalignment between experts, regulators, and users on whether and what data is 'sensitive', partly due to how data is presented to users; as single interactions. We investigate users' perspectives on the sensitivity and intimacy of their Google Assistant speech records, introduced comprehensively as single interactions, patterns, and inferences. We collect speech records through data donation and explore them in collaboration with 17 users during interviews based on predefined data-sharing scenarios. Our results indicate a tipping point in perceived sensitivity and intimacy as participants delve deeper into their data and the information derived from it. We propose a conceptualization of sensitivity and intimacy that accounts for the fuzzy nature of data and must disentangle from it. We discuss the implications of our findings and provide recommendations.

Designers and HCI researchers from industry and academia have been exploring the opportunities that emerge from incorporating behavioral data into the design process. For this, designers employ and combine data from multiple sources, multiple scales, and types to obtain valuable insights that inform and support design decisions. This combination unfolds through interdisciplinary collaborations, enabled by various methods and approaches, including participatory data analysis, sense-making interviews, co-design workshops, and data storytelling. However, due to the personal nature of behavioral data and the open-ended, iterative approach of HumanCentered Design, data-centric design activities clash with current HCI and data science practices. As both industry and academia increasingly use data-centric design processes, we recognize a need to share both examples and experiences to reinforce that most practices (and failed experiences) do not yet emerge solely from the literature. In this Special Interest Group, we aim to provide a space for design, data, and HCI researchers and practitioners to connect, reflect on the current practices, and explore potential approaches to further integrating behavioral data into design activities.