Trust me, I'm a scientist.

I am sure, as scientists, we have all uttered this phrase at least once. There is a certain pride and comfort that comes with claiming to be part of such an elite field of seemingly noble pursuers of knowledge, who altruistically devote their time to making discoveries and finding cures that will benefit mankind for years to come. But really, can we be trusted? I have felt my confidence in this shake every time a colleague has uttered this statement one time too many. Maybe it is the imposter syndrome speaking, but can we really claim to know all of the facts, especially in a discipline that is constantly challenging the status quo and reexamining the “facts” of yesteryear? (Need I remind anyone of how many iterations of the atom we have produced and we are still trying to get it right?)

I am not saying that scientists should not be trusted, but what we would most benefit from would be a change in the context of this phrase. Do not trust me to have all of the answers, but do trust me to approach the question with an open-mind towards a multitude of testable possibilities in the hopes of eliminating a few. Do not trust me to find the silver bullet, but do trust me to have a potential treatment for a subset of the tested subjects. Do not trust me to produce positive results that always support my hypothesis, but do trust me to honestly present my findings, the good, the bad, and the unsexy. This is the kind of trustworthy scientist I want to be, honest and enthusiastic, but nonetheless not blinded by my own bias towards being right.

But if it were simply our own biases we were fighting, this problem of unreliability in science would be manageable with our current peer-review system. Unfortunately, the push for flashy results that provide anecdotes to the human races’ biggest problems (cancer, anyone?) is all too strong of a pull to keep the biasness at bay. The publish-or-perish culture of scientific journalism and the hierarchy it creates is a powerful force that can make all but the most steadfast of scientists to conform to a system that rewards the biggest ripple makers, regardless of whether they are made with nuggets of truth.

So as scientists, let’s resolve to be trustworthy. But let’s do this by accepting our disproven hypotheses as worthwhile findings, by being okay with making only incremental steps towards cures, by welcoming critique of our experiments so that our research can improve. By knowing that the unbiased representation of all of our findings will set the foundation for a new set of truth to be laid.

Written in reaction to:

1.     Horvath, J. The Replication Myth: Shedding Light on One ofScience’s Dirty Little Secrets. Scientific American Guest Blog, Dec 4,2013

2.     Trouble at the lab: Scientists like to think of science asself-correcting. To an alarming degree, it is not. The Economist, Oct 19, 2013

3.     Belluz, J. Why you can't always believe what you read inscientific journals. Vox.com, Sept 1, 2015

4.  Dan Ariely's TED talk on deception

The Morality of Reproducible Data

            As academic scientists, we of course are invested in our research, and (ideally) want to leave our own mark on our respective fields. The data that we generate and publish do not just contribute to personal edification, but also the understanding of a topic on a global scale. With the wide accessibility of scientific journals, data is consistently reanalyzed and published findings applied to new experiments in a growing international research network. Keeping this in mind, it is more critical than ever before that the research community emphasize the importance of sound (and complete) data and experimental reproducibility.

            In his TED talk, Dan Ariely discussed how it was more likely for every person in a room to cheat a little than for just one to completely cheat. Students would give themselves a “4” in lieu of the “2” they deserved, presumably so that they would get a slightly better reward while still maintaining a degree of self-respect. This is sadly applicable in the research world, in the form of “cherrypicking” data or only withholding any negative findings from papers. Findings may often be smudged or spun in a certain light, or an incomplete story presented, not nearly enough to warrant a retraction, but just enough that the findings may not be entirely trustworthy. I agree with Jared Horvath’s Scientific American article in that funding provides a constant pressure for scientists to focus first on generating marketable data, and second on generating complete or valid data. However, while funding is a legitimate concern, I do not think that it is an excuse to perform unviable research or twist results. Granted, this is easy for me to say as a graduate student with a guaranteed stipend, but labs that produce questionable data do more than fail to contribute to science; they actually detract from ongoing research. False data can mislead other researchers who may use these findings as a baseline for their own projects, which in turn could possibly fail or lead to more misdirection. The withholding of negative data could lead other labs to pursue these and waste precious grant money rediscovering what should already be public domain.

            After reading some of these articles, it seems that it should be easier than ever to make sure that research is well-executed, given the formation of organizations, such as the PLoS ONE New Reproducibility Initiative and PubPeer. While these opportunities should be taken with a grain of salt, they seem like a viable means for experts to help fact-check or ensure that results hold true. I do think there is a critical difference between difficult and irreproducible experiments, in that some procedures may have a low success rate due to the necessity for high level of technical skill or specialized setup. However, if even a group of experts in the same field cannot recapitulate a finding, something is likely at fault with the underlying experimental strategy or the published data.