My graduate thesis in exercise physiology involved answering a research question that required collecting an awful lot of data before I had enough for analysis. I was comparing muscle fatigue in males and females, and in order to do this I had to find enough male-female pairs that matched for muscle volume. I took skin fold measurements and calculated the muscle volume of about 150 thighs belonging to men and women on the crew teams of Ithaca College. Out of all of that, I found 8 pairs that matched. It was hardly enough for grand findings, but it was enough to do the analysis, write my thesis, successfully defend it, and earn my degree. After all, that’s what research at this level is all about, i.e. learning how to put together a study and carry it all the way through to completion.
During my defense, one of my advisers asked, “With all of that data, you could have answered ___, too. Why didn’t you?” I hemmed and hawed for a bit, before finally answering, “Because that’s not what I said that I was going to do,” an answer that my statistics professor, also in attendance, said was the right answer. Was my adviser trying to trick me? I’m not sure, but it’s an experience that I remember often today when I read and talk and work in a field obsessed with the “data deluge.”
The temptation to do more than what you set out to do is ever present, maybe even more today than ever before. We have years worth of data – a lot of data – for the mammography study. When the grant proposal was written and funded, it laid out specifics regarding what analysis would be done; what questions would be answered. Five years down the road, it’s easy to see lots of other questions that can be answered with the same data. A common statement made in the team meetings is, “I think people want to know Y” or “Z is really important to find out.” The problem, however, is that we set out to answer X. While Y and Z may well be valuable, X is what the study was designed to answer.
I see a couple of issues with this scenario. First, grant money is a finite resource. In a time when practically all research operates under this funding model, people have a certain amount of time dedicated, i.e. paid for, by a grant. If that time gets used up answering peripheral questions or going down interesting, but unplanned, rabbit holes, the chances of completing the initial work on time is jeopardized. As one who has seen my original funded aims change over time, this can be frustrating. And don’t hear me saying that it’s all frustrating. On the contrary, along with the frustration can come some pretty cool work. The mini-symposium on data management that I described in earlier posts was a HUGE success for my work, but it’s not what we originally set out to do. The ends justified the means, in that case, but this isn’t always what happens.
The second issue I see is one that I hear many researchers express when the topics of data sharing and data reuse are raised, i.e. data is collected a certain way to answer a certain question. Likewise, it’s managed under the same auspices. Being concerned about what another researcher will do with data that was collected for another reason is legitimate. It’s not a concern that can’t be addressed, but it’s certainly worth noting. When I was finished with my thesis data, a couple of faculty members offered to take it and do some further research with it. There were some different questions that could be answered using the larger data set, but not without taking into account the original research question and the methods I used to collect all of it. Anonymous data sharing and reuse, without such context, doesn’t always afford such, at least not in the current climate where data citation and identification is still evolving. (All the more reason to keep working in this area.)
We have so many tools today that allow faster and more efficient data collection. We have grant projects that go on for years, making it difficult to say “no” to ask new questions of the same project that come up along the way. We are inundated with data and information and resources that make it virtually impossible to focus on any one thing for any length of time.
The possibilities of science in a data-driven environment seem limitless. It’s easy to forget that some limits do, in fact, exist.