Ethics in Research Part I

Ethics is such a challenging topic to not only write about and discuss, but also to just think about. This is especially the case in research and academia for much is based off of results: tenure, publications, dissertations, grants, and so much more. On the flip side, it is because so much rides on productivity and results that cause people to travel down a path that is ethically incorrect. It’s a discouraging day to learn when someone has sacrificed their ethical values in order to get published or progress into their graduate studies.

I have dedicated this first blog post to the discussion of data manipulation, which falls under the umbrella term of Ethics in Research. What comes to mind when you ponder data manipulation? Is it changing one number from 0.4 to 0.5 thus allowing it round up? OR could it mean deleting a data point that is a far outlier, but statistically cannot be discounted from your data set? These are questions that plague graduate students on a daily basis, and it’s really really quite unfortunate that they do. Graduate students already have enough stress in completing a rigorous 5+ year degree, that to infringe on their moral compass in order to get ahead slightly is almost a daily test they must face.

Data manipulation includes all of the scenarios above and encompasses many others beyond them. For those that either have never been in the science industry or it’s been a long time since you have, you may not realize how times have changed with respect to the speed by which data can be generated. For instance, in my lab, we can generate 9 hours worth of data, taking a data point every 0.016 minutes for an entire 9 hour study. And that’s only for one analyte, multiply it by 15 and that’s the total for a mixture that I work with on a daily basis. Seeing this vast quantity of data, it would be simple to just “overlook” a data point. Or see that the relative standard deviation is so close to 5%, if we could just delete one time point, we’d have our optimal value. **Note, these are things I don’t do, but could do. This is an example of the pressures on grad students to have the best data.**

As the years have progressed, science has more and more heavily relied on instrumentation in order to ascertain theories and proof of concepts. The more sophisticated the instrumentation, generally, the more parameters to change. Along those lines, the more parameters to change alternatively leads to more data generated ( as a per parameter basis). Obviously, this is almost like falling down the rabbit hole of science. More data equals more instances to ethically infringe upon, and right there lies the problem.

Not only are there more opportunities to manipulate data as the amount of data increases, but one also has to consider sources of stress. Stresses lie at the student, faculty, and institutional levels. In my opinion (and yes I come from a warped because I am a graduate student), graduate students have the highest levels of stress placed on them. They face stresses from themselves to push themselves as far as they can (to finish as fast as possible and to graduate as fast as possible), from their mentor to produce data, and the institution to graduate on time and in good standing. But graduate students are the ones that are also in the lab acquiring the data, so they are the ones that can manipulate the data. Unfortunately, many times, through the varying pressures that are placed on graduate students, they feel the need to force their results to show the hypothesis they expect. Many time, mentors will pressure students to make the data fit hypotheses, which is completely negating the entire process of research.

From my point of view, graduate school is a training ground for future researchers. It’s also a training ground for establishing one’s moral compass and values. When is it right to put one’s foot down and say “this is NOT what the data is representing.” It’s almost as if a doctorate degree is earned upon making one’s mark in his/her area of specialty all the while challenging hypotheses and developing a thick skin towards the pressure to produce what is desired and not what is seen in a research lab. It’s a development of one’s ethical values as a researcher and how far one will go to prove or disprove a point.

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