A case of scientific dishonesty has hit close to home and got me thinking. This isn’t a post of the details of the case (you can read more here if you’re interested) or the players involved (I don’t know them more than to say hi in the hallway) or to comment this particular case since I don’t have any more information than what is publically available. So if you’re looking for insider gossip, the following is bound to disappoint. Instead this example has got me reflecting in general about scientific dishonesty and what I can do about it.
The first obvious answer is that I can conduct my self and my research in an honest way. Sounds simple right? In theory it is. I can be accountable for my own actions of course but science is rarely a solo endeavour and I tend to collaborate. So it isn’t just me that I need to look out for.
So how do we make sure our research collaborations are honest? It is a tougher thing to address because there is a lot of trust in science. Even if you are involved with the whole process from inception to publication, if you aren’t personally doing the work it is possible that one of your collaborators is acting dishonestly. So I guess the next answer is that you should choose honest collaborators. Again, that is an overly simplistic answer to a complex issue which might not be possible to live up to. How do you know if your collaborators, students, etc. are honest? We often assume they are but clearly there are pressures to fudge data and get high impact publications in science or otherwise scientific dishonesty wouldn’t happen.
It is easy to say that as a co-author you should check that the results are valid but it can be more complicated than it sounds. Depending on the data, you might not have the expertise to ferret out any wrong doing for all the pieces in the collaboration–it is often why we collaborate in the first place! Or it might be difficult to independently verify all results with your own analyses. Often as student advisors we evaluate summary statistics and big picture rather than the raw data. Even if we traced all analysis from raw data to final product, I’m sure that if someone wanted to fake the data than they could also figure out how to hide that from their advisors/collaborators.
So how do we promote scientific honesty? Well, this is a really tough one. As was pointed out by my colleagues at our weekly lunch, it isn’t as if you can just say “don’t cheat/be honest” and all is good. Likely people who are looking to cheat the system will do it whether or not their advisors say anything about it.
Myself I have found that having shared folders where data, analyses and manuscripts live has helped me be more informed about what my students are doing. I hope that an offshoot of this approach is to also encourage honesty in my group because the raw data are available to trace back. It also helps with ensuring that data are backed up in multiple ways so they’re not lost. But I am wondering what more I can do to help my group maintain scientific honesty.
Ultimately I will continue to trust the work of the people I do science with until they show me otherwise because I don’t want to live or work expecting people to cheat. But I am also cognisant of the pressure in science to preform.
Since I’ve been applying to here in Sweden, the national granting agents have success rates in the 10-15% range but some go as low as 5%. There is an incredible pressure on young researchers to establish their careers. Few permanent positions come up and there are many who stay on after the post doc stage on soft grant money until those permanent slots open up. If you’re like me and miss securing funding when you need it, then you can be unemployed for a year. And the pressure in Sweden is no way unique; it is just the kind I’m most familiar with at the moment. With incredible pressure, I’m sure comes the temptation to cheat the system.
I don’t have great answers to how to solve issues of scientific honesty but personally I aim to foster a kind atmosphere in my group. I hope my lab is a place where it is ok to fail and make mistakes, where a career in academia is not seen as the only successful outcome, and where we can think creatively about our data when our hypotheses are not supported. I hope I never have to face true scientific dishonesty with any of my collaborations and that I can lead by example for those I train. But although I’ve been pondering concrete things I can do in my own lab, it is difficult to come up with an approach that can avoid scientific dishonesty entirely.
I’d love to hear other suggestions for fostering scientific honesty!