The sociology of data science is potentially more complex than that of most scientific communities. Data science brings together individuals from an unusually broad range scientific arenas, requiring the close collaboration of investigators from both highly quantitative/computational fields and fields that have historically been resistant to such approaches. We need to understand the motivations, challenges, and needs of methodological experts attempting to make meaningful contributions to problem domain areas to domain experts learning how to communicate the specific challenges in their areas. The world of data science is changing so rapidly and the interventions that will be required to address data science challenges will need to be innovative and responsive. Understanding the complexity of the landscape and integrating this knowledge with study designs that can evaluate the extent to which our efforts are driving the desired results will require a complex interplay between quantitative and qualitative approaches that is at the frontier of existing “mixed methods” strategies.
The goal of this working group is to understand how the social practices, institutions, and culture of the data science community interact with the tools we need to build and the skills we need to develop to facilitate the production, creativity, and success of data-intensive science.
Ethnographers at UC Berkeley, New York University, and the University of Washington will characterize the intellectual, institutional, and cultural landscape and highlight the complex processes by which research and collaborations evolve; careers progress; and “students” (from all disciplines and from all points of the career path) learn and use knowledge, skills, and tools. This working group will map this evolving landscape as practices change, identify the ways new activities are able or unable to achieve their goals, and then modify existing practices or develop new interventions as needed.
