The integration of the data scientist into the team: implications and challenges

Tracking #: 437-1417

Authors:

NameORCID
Manisha DesaiORCID logo https://orcid.org/0000-0002-6949-2651


Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper

Abstract: 

Modern biomedical research is complex and requires a cross section of experts collaborating using multi-, inter-, or transdisciplinary approaches to address scientific questions. Known as team science, such approaches have become so critical it has given rise to a new field – the science of team science. In biomedical research, team-based collaborations have great need for data scientists. Integration of data scientists into research teams has multiple advantages to the clinical and translational investigator as well as to the data scientist. Clinical and translational investigators benefit from having an invested dedicated collaborator who can assume principal responsibility for essential data-related activities, while the data scientist can build a career developing tools that are relevant and data-driven. Participation in team science, however, can pose challenges to the promotion of the data scientist. One particular challenge is the ability to appropriately evaluate the data scientist’s scholarly contributions, necessary for promotion. Only a minority of academic health centers have attempted to address this challenge. In order for team science to thrive on academic campuses, leaders of institutions need to hire data science faculty for the purpose of doing team science, with novel systems in place that incentivize the data scientist’s engagement in team science and that allow for appropriate evaluation of performance. Until such systems are adopted at the institutional level, the ability to conduct team science to address modern biomedical research with its increasingly complex data needs will be compromised. Fostering team science on campuses by putting supportive systems in place will benefit not only clinical and translational investigators as well as data scientists, but also the larger academic institution.

Manuscript: 

Tags: 

  • Reviewed

Data repository URLs: 

None

Date of Submission: 

Thursday, March 30, 2017

Date of Decision: 

Tuesday, April 25, 2017

Decision: 

Undecided

Solicited Reviews: