Abstract

Data science has been heralded as a transformative family of methods for scientific discovery. Despite this excitement, putting these methods into practice in scientific research has proven challenging. We conducted a qualitative interview study of 25 researchers at the University of Michigan, all scientists who currently work outside of data science (in fields such as astronomy, education, chemistry, and political science) and wish to adopt data science methods as part of their research program. Semi-structured interviews explored the barriers they faced and strategies scientists used to persevere. These scientists quickly identified that they lacked the expertise to confidently implement and interpret new methods. For most, independent study was unsuccessful, owing to limited time, missing foundational skills, and difficulty navigating the marketplace of educational data science resources. Overwhelmingly, participants reported isolation in their endeavors and a desire for a greater community. Many sought to bootstrap a community on their own, with mixed results. Based on their narratives, we provide preliminary recommendations for academic departments, training programs, campus-wide data science initiatives, and universities to build supportive communities of practice that cultivate expertise. These community relationships may be key to growing the research capacity of scientific institutions. 

Keywords: scientific research, computing education, collaboration, mentorship, career paths, academic

02/09/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©202X First Author, Second Author, and Third Author. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.