Training Interdisciplinary Data Science Collaborators: A Comparative Case Study

Abstract

Data science is inherently collaborative as individuals across fields and sectors use quantitative data to answer relevant questions. As a result, there is a growing body of research regarding how to teach interdisciplinary collaboration skills. However, much of the work evaluating methods of teaching statistics and data science collaboration relies primarily on self-reflection data. Additionally, prior research lacks detailed methods for assessing the quality of collaboration skills. In this case study, we present a method for teaching statistics and data science collaboration, a framework for identifying elements of effective collaboration, and a comparative case study to evaluate the collaboration skills of both a team of students and an experienced collaborator on two components of effective data science collaboration: structuring a collaboration meeting and communicating with a domain expert. Results show that the students could facilitate meetings and communicate comparably well to the experienced collaborator, but that the experienced collaborator was better able to facilitate meetings and communicate to develop strong relationships, an important element for high-quality and long-term collaboration. Further work is needed to generalize these findings to a larger population, but these results begin to inform the field regarding effective ways to teach specific data science collaboration skills.

KEYWORDS:

• Case study
• Data science collaboration
• Data science education
• Statistical consulting
• statistical practice

Acknowledgments

The authors report there are no acknowledgments to declare.

Data Availability Statement

The data that support the findings of this study are openly available in the Open Science Framework at https://osf.io/c95jd/.

Disclosure Statement

The authors report there are no competing interests to declare.

Notes

1 We purposefully chose the ordering of the meetings to favor the more experienced collaborator. Since the domain expert met with the students first, by the time she met with the experienced collaborator, she had a better idea of how to explain her project to someone in another field and could anticipate questions or points of confusion. To this end, we expect the data to be biased in favor of the experienced collaborator and that evidence suggesting success for the students to minimize the extent of that effectiveness.

2 Due to the ways the collaborators explained the multiple collaboration meetings, we were unable to blind the transcripts regarding student or experienced collaborator status and still code for all elements in Table 1.

3 Authors from a research and evaluation center on campus, who are not affiliated with LISA, developed the rubric based on the ASCCR frame and piloted it with five coders across four disciplines outside of LISA and four collaboration videos. Raters independently scored videos in teams of two and then met to discuss necessary changes to the rubric to appropriately identify differences in quality across score levels as well as to clarify language in the rubric.

4 Full transcripts are available on the Open Science Framework (OSF; https://osf.io/c95jd/) for readers to use with the included rubrics and paper text to better understand the coding, reproduce the results, and repeat the evaluation process in other contexts.

5 Full survey available at (https://osf.io/c95jd/)

Additional information

Funding

This work was supported by the National Science Foundation under Grant No. 1955109 and Grant No. 2022138 for the projects, “IGE: Transforming the Education and Training of Interdisciplinary Data Scientists (TETRDIS)” and “NRT-HDR: Integrated Data Science (Int dS): Teams for Advancing Bioscience Discovery.” This work was also supported by the United States Agency for International Development under Cooperative Agreement Number 7200AA18CA00022 for the project, “LISA 2020: Creating Institutional Statistical Analysis and Data Science Capacity to Transform Evidence to Action.”