Data science ethnography

Our team of ethnographers is studying how researchers work with large and complex datasets, and the institutions, programs, and communities that support data-intensive research. We embed ourselves in the places people are using and learning data science methods in order to understand how different communities make sense of and value data, and what is organizationally required to support data intensive practices and collaborations.

Traffigram: A design methodology for distance cartograms

Mapping technologies have the potential to help people better understand their transportation options and habits by conveying information about travel time, carbon dioxide emissions, expense, calories burned, and other metrics associated with transportation. The purpose of this research is to develop a new methodology for designing user-centered distance cartograms, and to build a platform that can be widely used by users, designers, researchers, and practitioners all around the world.

Emotion and affect in distributed collaboration

Distributed collaborative teams increasingly rely on online tools for interaction and communication for both social and task-oriented goals. We are expanding recent work linking emotion and affect to collaboration and creativity in order to model how this collaborative communication takes place by examining real-world examples, specifically from the chat logs of an international astrophysics collaboration.

Through qualitative analysis of the text-based communications created by these online collaborative projects, we aim to understand how team members express affect and emotion in this medium and the impact that these expressions have on group dynamics, creativity, and problem solving.

Visual analytics for chat and social media

Large online communication and social media datasets are increasingly used by researchers in many disciplines, but the size, complexity, and volatility of these datasets presents a challenge. We are designing and building visual analytics tools, combining interactive visualizations with computational modeling, to help researchers explore and understand these datasets more easily. Challenges include effectively summarizing and slicing large amounts of text-based data, surfacing contextual information, and integrating different kinds of metadata. Through our human-centered design approach, in which we work closely with practitioners of social media research, we have prototyped and tested several new social media analysis systems, and uncovered opportunities and implications for design.

Tools for qualitative analysis of online communication data

Researchers working with social media and online communication data often apply mixed methods, including both quantitative and qualitative analysis. While statistics and computational modeling can reveal general patterns over large datasets, qualitative analysis can generate rich descriptions and theory. By combining both approaches, researchers get the strengths of both. However, qualitative analysis, specifically coding, requires manual human interpretation and is very labor-intensive. Technology can help make this process more efficient. Our group has developed a web-based tool for qualitative coding of large chat datasets, and machine learning tools to “amplify” manual coding on a subset of data, by learning categories that can be applied over the entire dataset.

Previous projects

Collaborative games for bioinformatics education

We designed, implemented, and released an online, multi-player collaborative educational game that incorporates bioinformatics and cyberinfrastructure (CI) concepts aimed at high school students.

We are interested in the uptake of concepts of cyber problem solving specifically among young underrepresented minorities and women, and in better understanding the larger relationships between people, educational games, and infrastructural computational technologies. Collaboration and creative strategies are encouraged and integrated into the gameplay mechanics.

Human-centered biometric security

In the design of security systems, technical and security requirements usually drive decisions, but in practice, a security depends on a complex network of human and technical resources. Insufficient attention to human issues ranging from usability to organizational context can make even the most carefully engineered security system worse than useless.

We study security technology from a human-centered perspective. Our work includes usability evaluation of emerging security technologies, studies of people’s day-to-day password-related practices, and evaluation to determine the key interface design factors affecting user acceptance of biometric security systems.

Collaborative Creativity

Contrary to the popular belief of the “aha” moment of insight, recent work has indicated that creativity is often a series of incremental steps to discovery. As an idea is developed, it is amplified over time in its social context.

Dr. Aragon and her colleagues are developing and evaluating a dynamical systems theory of collaborative creativity based on distributed affect and interfaces that facilitate socio-emotional communication.

Thermostat Usability

Residential thermostats control about 10% of national energy use. In 2008 Energy Star concluded that homes with programmable thermostats were using more energy than homes with manual thermostats. As a result, Energy Star terminated the thermostat endorsement program in 2009 and decided that any future endorsement program must include specifications for minimum levels of usability. HDS Lab Director Cecilia Aragon and her colleagues performed multiple lab and field studies of thermostats and developed an innovative usability metric for thermostats to facilitate energy saving behavior. This metric is being evaluated in Energy Star’s draft specifications for programmable thermostats.

Sunfall - Visual Analytics for the Nearby Supernova Factory

Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In order to address this problem for observational astrophysics, we built Sunfall, a collaborative visual analytics system for supernova discovery and data exploration.

Airflow Hazard Visualization

Dr. Aragon and her colleagues developed an airflow hazard visualization system for helicopter pilots. In a flight simulation usability study, the system significantly reduced the simulated crash rate (from 19% to 6.3%) among experienced pilots flying a high fidelity, aerodynamically realistic fixed-base rotorcraft flight simulator into hazardous conditions.

This work highlights the importance of understanding principles of human visual perception and cognitive ability, and applying this knowledge to the design and implementation of appropriate visualizations in an operationally stressful environment.