The interdisciplinary nature of modern science adds to significant gaps in scientists’ performance caused by limited proficiency levels with diverse scientific tools and a lack of common language across different disciplines. Although developers of data intensive science platforms are slowly beginning to move away from function-oriented software engineering approaches and towards to user-centered design approaches, they rarely consider users’ value, and expectations that embrace different user contexts.

Further, there is an absence of research that specifically aims to support the broad range of users from multiple fields of study.  Thus, a goal of this research is to investigate scientists’ experiences with interdisciplinary data-intensive knowledge resources and derive design implications for delivering consistent user experiences. 

For this work, we examine:

  • Implicit and explicit motivations for knowledge sharing
  • Predictive models of online human behavior
  • Data representation and visualization techniques to match users’ cognitive styles
  • Adaptive interfaces that support steep learning curves with powerful & complex scientific software tools

Select Publications

Rapid Interdisciplinary Ideation & Sketching

Rapid Ideation & Sketching
Rapid Interdisciplinary Ideation & Sketching

Employing Eye Tracking to Inform Iterative UI Design

Employing Eye Tracking to Inform Iterative UI Design
Employing Eye Tracking to Inform Iterative UI Design

2D Interactive Visualization of Big Data

2D Interactive Visualization of Big Data
2D Interactive Visualization of Big Data

Modelling Users' Motivations for Knowledge Sharing in Data Intensive Sciences

Modelling Users' Motivations for Knowledge Sharing in Data Intensive Sciences
Modelling Users' Motivations for Knowledge Sharing in Data Intensive Sciences