Last week was a busy one at the U.S. Office of Research Integrity (ORI), at least judging by how many cases they posted. There were sanctions against researchers at Ohio State, Texas Tech, and the Gladstone Institutes, as we reported. And it turns out there was another case closed, of a former Harvard dental school research fellow, The Scientist reports.
According to the ORI, Martin Biosse-Duplan “engaged in research misconduct in research supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health (NIH), grant R01 AR054450.”
The misconduct involved a lab presentation and two published abstracts:
- Boisse-Duplan, M., Stephens, S., Lai, F.P.L., Oelkers, M., Kitamura, D., Rottner, K., Horne, W., & Baron, R. “The Association Between the Microtubule Plus End Protein EB1 and Cortactin Controls Podosomes and Bone Resorption.” J Bone Min Res 26:Supl.1, pS215.
- Boisse-Duplan, M., Stephens, S., Lai, F.P.L., Oelkers, M., Rottner, K., Horne, W., & Baron, R. “In Osteoclasts, Dynamic Microtubules and their Associated Protein EB1 Control Podosomes and Bone Resorption through Cortactin.” Bone 48:Suppl. 2, pS97.
According to the ORI:
As a result of HSM’s and HSDM’s investigation, the data were not presented at the meetings and the experiments reported in the abstracts are being redone.
Specifically, ORI finds that Respondent:
Falsified Powerpoint slides and spreadsheets for histomorphometric and microCT results by using the values of HS1 knockout (KO) mice and their controls to represent the CathepsinK cre- Cortactin KO mice and their controls; Dr. Biosse-Duplan also switched two sets of numbers between the HS1 KO mice and their controls to falsely demonstrate a difference in bone density when there was none. The numerical data were presented at a lab meeting, and false text was included in two submitted meeting abstracts published in Bone 48:Suppl 2, pS97 and J Bone and Mineral Research 25:Suppl 1, pS215.
Boisse-Duplan has agreed to have any research supported by the Public Health Service — the umbrella agency of the NIH — supervised for two years, and to a two-year ban on serving on NIH peer review committees.
ORI has made no bones about their disapproval of what Boisse-Duplan did…
“Dr. Biosse-Duplan also switched two sets of numbers between the HS1 KO mice and their controls to falsely demonstrate a difference in bone density when there was none” – scientists are supposed to pursue the truth and this guy did find it but, for some reason, did not like it. It is worse than not knowing the truth and fabricating data to promote a certain hypothesis.
Another slap on the wrist. From “punishments” like these, one would imagine that honest scientists needing money to conduct valid research are in short supply, so the feds don’t want to be too harsh with the fraudsters or there will be nobody left to whom research money can be given. 🙂
As for JudyH, it’s really hard for me to understand the nature of the “punishment”:
1- “…to have any research supported by the Public Health Service supervised for two years”. So, in normal circumstances, research is NOT supervised ? Nobody checks for data, results, and conclusions ???
2- “…a two-year ban on serving on NIH peer review committees”. This is clearly a reward for cheating with data. The time saved by this way will be used, I hope, to develop good and fruitful Science.
It does seem that punishment doesn’t fit the “crime”. If the taxpayer realised that not serving on committees was a bonus, they would really wonder what is going on here.
As for results and conclusions, in principle, once published they are open to question. Problem is, most don’t question much anymore.
Hello all,
Please read:
“Dr. Biosse-Duplan also switched two sets of numbers between the HS1 KO mice and their controls to falsely demonstrate a difference in bone density when there was none”
If there is no difference to begin with, how can switching sets of numbers between groups create a significant difference, or a prominent effect?
Maybe he switched the numbers that didn’t fit into the group (e.g. the larger numbers in group 1 go to group 2 because there’s more larger numbers in group 2 and the smaller numbers in group 2 go to group 1. That way there’s a difference).