How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

TL;DR

This study explores the challenges and dynamics of collaborations between data scientists and biomedical domain experts, emphasizing the importance of content and process common ground for successful scientific outcomes.

Contribution

It provides empirical insights into collaboration tensions and proposes that strengthening process common ground benefits scientific discovery.

Findings

Collaboration tensions influence outcomes

Content and process common ground are crucial

Breakdowns in content ground can be mitigated by process ground

Abstract

In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.