What is the principal investigator’s responsibility regarding data authenticity?

Question

Every now and then, you hear stories of data being fabricated by a graduate student (or post-doc) who felt the pressure to publish groundbreaking results was too much. This unethical behavior makes me wonder: What can a PhD advisor (or group leader, or project principal investigator) actually do to avoid that in her own group? What are her ethical duties in ensuring that all published data is genuine?

Please note: I'm not singling out graduate students or post-docs because I think they are statistically responsible for more ethical misconducts than others… only because it gives a good case of when the PI would have only indirect access to the data (meaning: she didn't actually do the experiments herself, but was only presented the data by others in her group).

Answer 1

The PI is responsible for the output of the scientific employees under them, while they are doing work for the PI. (I think it would be unfair to hold a PI responsible, for instance, for quality control on the work done by a post-doc on a paper submitted with his or her former group.)

From an ethics standpoint, however, the PI is responsible for encouraging an atmosphere in which errors are caught and corrected, rather than tolerated. If errors are "innocent" in nature, then no guilt or punishment should really follow from catching and fixing those errors. However, a PI is responsible for not sanctioning deliberate lapses. If the PI sets up a culture in which such behavior is viewed as expected or necessary, such failures do lie on the PI.

I would argue that the PI's responsibilities extend to ensuring that the work claimed has been performed, and that the data has been correctly analyzed. Some places I've worked at have instituted quality control procedures for doing so, to varying degrees of formality. While I don't think a full review of all of the data is often required in academic settings, I think most groups would benefit from some implementation of such measures. As Ana suggests, if you do "random" sampling of the work produced, then it makes it that much harder to falsify anything, since you don't know if that will be the work that will be checked.

A PI shouldn't be expected to fall on her sword for a single incident involving an underling. The PI could perhaps be castigated for making a poor personnel decision, but it shouldn't be a career-ender unless the PI is aware of and condones the unethical behavior. Cases such as the Bhrigu tampering case at Michigan, in which a postdoc tampered with the work of a graduate student in the same group, and which led to the PI moving to another university, are unfortunate, and only serve to make things more difficult for everyone. But the aversion should not have been cast upon the PI—which seems to have happened here.

Answer 2

We've talked about this at the institute where I'm doing my PhD, and the best solution seemed the following:

First, make a central database for all raw data, that can be accessed per request. Upload/copy data to it as soon as it's collected, including noisy data that might not enter the final analysis. This ensures that any excluded data has to be properly justified.

Second, let everybody know that every so often a random dataset will be pulled out and some basic checks run on it.

Third, run those basic checks. For this you need someone with knowledge of statistics who can tell you what is appropriate for your type of data. A lot of basic aspects of normal data are difficult to simulate without a lot of knowledge on statistics. To give an example, if you take data that have a normal distribution, split it into quintiles, then plot the mean against the variance in each quintile, they should roughly fall on an inverted U-shaped curve. I know of a case where this relationship was perfectly linear, which raised alarm bells. In any case, these checks should be simple and easy to run.

Fourth, decide who will do these checks, because it takes time and effort.

Fifth, make sure you have some idea of what types of mistakes are honest mistakes, and what constitutes actual fraud. Make sure you discuss mechanisms of dealing with these mistakes (and fraud) beforehand, i.e. don't leave this decision in the hands of the supervisor at the moment it happens.

Edit: I guess the main question was what's the PI's responsibility. My reply reflects my opinion that the responsibility should be more institutionalized, and not left just to the PI. On the other hand, the PI could run some of these steps internally if needed / if there is no other help available. But then it might be too elaborate, so perhaps you will get some better answers from other people.

Answer 3

It's a sad reality. That is why research group leaders should be so deeply involved in their post-graduate students' research that they can detect fabrication of data easily. It is unfortunate when research group leaders only sit in their room and expect the student to do everything for them and put their name for the paper. It is the duty of the leader to scrutinize and question the results and ask for repeats when necessary and go carry out a few experiments too to authenticate results before accepting them.