My view on “An incentive-based approach for improving data reproducibility”

This week Science Translational Medicine published the commentary by Michael Rosenblatt, Chief Medical Officer at the big Pharma company Merck & Co., addressing the problem of research data reproducibility.

He correctly pointed out that “The problem of [research] data that cannot be replicated has many potential origins: pressures to publish papers or secure grants, criteria for career advancement, deficiencies in training, and nonrigorous reviews and journal practices, with fraud an infrequent cause”.

He proposed that one way to improve confidence and reliability of translational research would be “if universities stand behind the research data that lead to collaborative agreements with industry” and “In the instance of failure [i.e. irreproducible data]” “what if universities offered some form of full or partial money-back guarantee [to industry partner]?”

The main starting point for this proposal is the fact that currently “industry expends and universities collect funding, even when the original data cannot be reproduced.”. “Compensation clause” proposed by Michael Rosenblatt is an attempt to put certain additional “accountability” [call it “incentive” if you prefer] on universities’ shoulders.

Would such arrangement work? Unlikely, in my opinion. Why? By accepting such arrangement, it naturally would imply  that academic scientists are less than “good” in their work. It would suggest that university does not have a confidence in their own scientists. It would ultimately impinge on academic freedom by dividing scientists between reliable and non-reliable ones (in my view, simple double-blind peer review of scientific manuscripts would greatly improve the quality of the academic research).

In addition, this proposal also somehow wants to unnecessary ease the burden on industry bosses who are ultimately responsible for selection of the best academic projects for commercial purpose.

These are few examples why it would be extremely sensitive subject to implement. I don’t have illusion that data reproducibility issue has simple solution. One aspect that is not even mentioned in this commentary is whether our heavy reliance on animal [mouse] models is misplaced and is actually one of the main causes for failure to “translate” into human research.

posted by David Usharauli

One response

  1. […] for improving data reproducibility” published in Science Translational Medicine. Me and many of others do not agree on […]


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