Are you liable for misconduct by scientific collaborators? What a recent court decision could mean for scientists

Retraction Watch readers may have followed our coverage of the case of Christian Kreipke, a former Wayne State researcher who was recently barred from U.S. Federal funding for five years. That punishment followed years of allegations and court cases, along with half a dozen retractions. The case has been complicated, to say the least, and led to a 126-page decision by a judge last month. Here, Boston-based attorney Richard Goldstein, who represented the scientist in Bois v. HHS, the first case to overturn a funding ban by the U.S. Office of Research Integrity (ORI), tries to explain what it could all mean.

Can you commit research misconduct if you fail to detect false data from another scientist?

The answer is yes and here’s how it can happen.

You work in a well-regarded laboratory that receives government funding. You are frequently a principal investigator (PI) and a lead author. The lab suffered from some disorganization so when you took over, you demanded quality work and hired a new lab administrator.

Things are generally good but life in the laboratory is demanding.  The size of the lab makes it impossible for you to validate every piece of data.  So, you often have to trust that a colleague’s work is reliable and truthful, including from collaborators at other facilities.  Funding, as always, is a problem, which means you can’t buy enough equipment and data security software; tracking who did what is difficult.  Some lab employees (inherited from your predecessor) have professional or ‘personnel’ issues and you suspect some will leave the laboratory. And of course, there is growing pressure to publish, attend conferences, make new findings, and to keep the funding stream going.  There is never enough time.

All of that probably sounds familiar, but here’s where our story takes turn for the worse.

One day, you are summoned to a meeting with the institution’s research integrity officer (RIO) and told some data in a paper are falsified and they began a misconduct investigation. You are shocked.  You know the data but didn’t validate them personally before publication and you don’t know precisely who did the work.

One always worries about errors, but deliberate falsification and misconduct?  You are angry but soon become uneasy as you wonder why you weren’t the first to hear about the problems with the data.  Your shock and unease turn to dread when the RIO looks you square in the eye, hands you a sheaf of documents, and says you are being charged with misconduct, being placed on leave, and must immediately turn all over all of your files, data, and laptops.

As the weeks go by, you learn you are the only person in the lab under investigation. You maintain your innocence and no evidence ever emerges that you falsified the images or knew they were false.  You have your suspicions as to who falsified the data, but the dean doesn’t seem interested. No one else is charged. It is starting to feel like you’re being railroaded.

After hiring a lawyer, you protest this “selective prosecution.”  You also alert the administration to the waste of research funds and financial mismanagement. Your institution finds you – and only you – committed misconduct. You are terminated.

Years pass, lawsuits and hearings pile up.  You win a victory when your employer is found to have retaliated against you — but the misconduct finding stands. The U.S. Office of Research Integrity (ORI), adopting the institution’s report, brings formal misconduct charges and bars you from receiving Federal funding for 10 years.  How is this possible if you had no knowledge the data were false? You believe it is wrong and appeal, hoping a neutral judge will rectify this injustice.

The administrative law judge (ALJ) agrees to hold a hearing, the first such ALJ hearing in over a dozen years, and after months of waiting and more briefs and legal fees, he issues a 126-page decision.

Your debarment is upheld, although the judge reduces it to five years.

Not just a hypothetical

If you’ve been a regular reader of Retraction Watch, you will recognize this as the case of Christian Kreipke, a researcher formerly at Wayne State University in Detroit.  Although the long-running Kreipke case has been discussed previously, the ALJ’s May 2018 decision has something important for everyone. I suspect that – for many years to come – it will be referred to by ALJs, by ORI, by university administrators, by RIOs, by investigation panels, by science journalists, and by lawyers.

However, there is something especially important in the decision for senior scientists, especially PIs and lead authors.

As a lawyer who represents scientists in misconduct cases, one of the most difficult issues is the responsibility of lead authors and PIs for work done under their supervision.  Although the Kreipke decision is important for many reasons, this part of the decision could affect lab practices everywhere.

Perhaps the most significant fact in the case is that Kreipke did not falsify or fabricate any data.  Nor did he know any data was false. The first time he learned of false or fabricated data was when he was told by the RIO after the commencement of the investigation. Kriepke’s problem was that he didn’t detect the falsification done by another scientist.

Nevertheless, the ALJ concluded, without much difficulty, Kreipke committed misconduct.  Nor did it matter to the ALJ that Kreipke was the only one charged with misconduct.

How is this possible?

What it means to be “reckless”

Followers of research misconduct cases will know that a scientist commits misconduct merely if he or she “recklessly” allowed the inclusion of false or fabricated data.  But what does it mean to be “reckless?” The Kriepke decision is important because it puts some “meat on the bones” of this legal term.  The ALJ said that including false or fabricated data without validating its accuracy is reckless if one “used materials without exercising proper care or caution and disregarded or was indifferent to the risk that the material were false, fabricated, or plagiarized.” It is not a defense if you assumed others performed research reliably and truthfully reported those results to you. As far as the ALJ was concerned, Dr. Kreipke didn’t do enough to validate the data.

After Kreipke, can one rely on the work of others?  One of the witnesses said “[We’ve] known each other for 25 or 30 years.  And because of that association, I would have accepted what came out of that laboratory without question unless I saw something.” Even though this kind of trust was a common practice, the ALJ decided it was not a defense because it may result in false or fabricated data. A PI or corresponding author cannot just accept “on faith” the data were correctly labeled and were accurate representations, even if it was coming from a longstanding collaborator or a trusted scientist in one’s own lab.

The ALJ said that an author, editor, other contributor is not presumptively liable for false material just because their name is on a grant application or article.  However, if one is a PI or first author, that person is presumably responsible for the content of the work.

Higher validation standards?

What does it mean?  More importantly, what do PIs and lead authors now have to do to protect themselves from allegations of misconduct?  Can any data be trusted or does the PI or lead author have to verify everything personally?

Some would say the Kreipke decision sets an unreasonably high standard for validating research on a collaborative project and upsets long established norms.  Others might say the ALJ got it right and Kreipke, as a lead author and PI, had a personal responsibility to police the work of others and to ensure it was accurate and verifiable.

What is now clear is that senior researchers, lab supervisors, PIs, and lead authors can no longer accept the work of collaborators at other labs or, for that matter, the work of scientists in their own lab, without insisting on (or conducting) some level of validation.  More importantly, even if there has not been a hint anyone’s work is suspect, the senior scientist, PI, or lead author must demand the work be validated. In a misconduct case, the lab’s procedures for verifying data will now come under close scrutiny. The Kreipke case establishes that a PI, lead author, or lab head who fails to validate data or employ adequate validation procedures may be personally liable for misconduct, even if the scientist had no knowledge of falsification or fabrication.

Accuracy in reported research is a core value.  Scientists entrusted with government funds should take all necessary steps to ensure research issued under their name is correct.  The possibility of a research misconduct finding creates a powerful incentive for scientists in a supervisory capacity to demand valid and accurate data.  The Kreipke case shows that a scientist is at risk for a research misconduct allegation if he or she does not validate data, even if it comes from a long time collaborator or a trusted colleague.


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