Experiences with a Flexible User Research Process to Build Data Change Tools

• Main Authors: Drew Paine, Devarshi Ghoshal, Lavanya Ramakrishnan
• Format: Article
• Language: English
• Published: Ubiquity Press 2020-09-01
• Series: Journal of Open Research Software
• Subjects: user research; ethnography; usability testing; mockups; experience report; data change
• Online Access: https://openresearchsoftware.metajnl.com/articles/284

Scientific software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientific knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientific end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientific software development work. The use of user research techniques in scientific environments can be challenging due to the nascent, fluid problem spaces of scientific work, varying scope of projects and their user communities, and funding/economic constraints on projects. In this paper, we reflect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientific environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes reflections and the lessons we have learned from our experiences. We offer key takeaways for scientific software project teams to effectively and flexibly incorporate similar processes into their projects.