The best and worst of times

When I started this blog I wanted to provide an honest description of what a starting faculty member goes through. I know that my experiences are probably representative of only a small percentage of TT faculty, but at least it's one honest and real-time perspective that isn't the revisionist history of more senior colleagues who offer advice. I've tried to stick to that as best I can without getting myself into trouble and hopefully it's been helpful to some who are on a similar path. As more readers have shown up, several of whom know who I am, it's gotten a bit harder for me to discuss certain things in a public forum. I'll be honest, there have been times when I've had to make myself talk about my failures. No one likes to air their dirty laundry, but I've convinced myself that letting other junior faculty who are running into the same roadblocks know that others are going through the same thing is valuable. As a postdoc I wouldn't have expected to go through as many proposals as I have to try and get funding, but here I am. The perils of starting a completely new line of research to start one's career.

In any case, last night I got to thinking about all the data we have coming in a few days and realized that I both love and hate these times. The waiting sucks but before the data arrive there are infinite possibilities. Everything could work as we hope and we could turn these data right around to high impact papers... or it could be crap and I would have been better off lighting a wad of cash on fire to warm my office. To make the stakes even higher, we're expecting critical data for the two major projects in the lab. If they turn out to not be very useful then I have some serious re-thinking to do, two student's with flailing projects and a lot less money to fix the problem with.

Yes, this is what the job is and I realize that. I know that if I always take the safe route we'll never make the big jumps we need to in order to push the edge of the field. However, my safety net isn't huge, so my margin for error is slimmer than some. The reality of huge datasets is almost always something in the middle of what you expect - useful but incomplete and needing some follow-up to make a full story. In this case, I would happily settle for just a small indication that we are not chasing a unicorn and that I haven't picked the wrong page in this chose-your-own-adventure story.