Maintaining intellectual diversity in data science

Tracking #: 432-1412


Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper


Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment.



  • Reviewed

Data repository URLs: 


Date of Submission: 

Sunday, March 5, 2017

Date of Decision: 

Monday, March 27, 2017

Nanopublication URLs:



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