Washington Post reported today on a saga of academic struggles unfolding at this time on campus at Johns Hopkins University.
A Chinese American scientist Dr. Daniel Yuan should have got the hint when he was demoted by Dr. Boeke after he questioned the data published by the Boeke Lab of Johns Hopkins University in Baltimore, Maryland. While he refused to keep silence, Dr. Boeke fired him. In the past few years, Yuan had informed Nature where the Hopkins papers published as well as NIH where the Hopkins team received millions of grants on the fishy research project. However, both Nature and the NIH refused to respond.
The Boeke Lab at Johns Hopkins specializes in genetic, otherwise dubbed 'factory science' by Nobel Laureate Dr. Sydney Brenner. In this type of research, large amount of data were produced by computerized equipments. A statistician would then run different models on these data, and hopeful some hidden information would be digged out. In plain English, the success of an experiment is often determined by advanced tricks applied in statistical analysis. Dr. Yuan was a statistician worked in the lab, who came to this research field out of pure love to science. His previous career was a pediatrician and assistant professor at Johns Hopkins University. When Dr. Yuan reran some data produced by others in the group, the numbers simply do not add up.
Johns Hopkins University obviously was not happy to hear anything negative about its star researcher, not named in the Washington Post article, Dr. Jef D. Boeke. Dr. Boeke is the PI of the project associated with the fraudulent data. Another Chinese scientist in the same lab Dr. Zhang Jie who challenged the data separately was also forced out by Dr. Boeke. While all the clues led to Dr. Boeke, it's doubtful how long could he keep the secret without some help from higher up.
In any typical year, Johns Hopkins University would receive $600 million from the NIH alone. The bulk of the NIH grants are on various -omics, which happened to be categorized by Dr. Brenner as 'high throughput, low input, and no output' biology. If the quota was proved to have somewhat truth in it, then the entire functioning structure of the NIH would collapse.
The coverup had been successful, until one group member Dr. Yu-yi Lin, who allegedly forged the data, found dead on the deadline he was supposed to answer questions to the data. Before the death made headline news, Yuan had asked the US federal government who paid for the fraudulent research to investigate, but Office of Research Integrity said, the incident "no longer pose a risk" to federal research after the researcher's suicide.
One thing for sure, as long as Johns Hopkins University has been happy with federal dollars, the government could care less. After all, it's not federal dollars. It is tax dollars that grows from the trees.
EVen with a relative small size, Johns Hopkins University has been the top recipient of federal research fundings for over 30 years. Year after year, Johns Hopkins University topped much much larger schools such as Harvard University or Stanford University in terms of gaining research grants. For example, in FY 2010, Johns Hopkins University scooped in $2 billion, with $1.73 of which come from the National Science Foundation (NSF), the National Institute of Health (NIH), The Department of Defense (DOD) and National Aeronautics and Space Administration (NASA).
Dr. Boeke's Lab is exemplary of Johns Hopkin's success, although it may be only the tip of the iceberg.
Wait, hold on a second, have we used the word 'suicide'? Perhaps not. Hours after Dr. Lin's death, an email was sent from Dr. Lin's email account to Dr. Yuan bragging about it. Dr. Yuan was laying with empty vials of sedatives and muscle relaxants around him. As far as we know, Dr. Yuan kept a copy of this email, but nobody seems bother/allow a criminal investigation. Anyone say murder? With billions of dollars at stake, what would you expect?
When contacted by the Washington Post, Johns Hopkins University declined to have any JHU employee interviewed.
We may never know.