Time to publish?
Although not quite the first time I've been told that I'm slacking in my life, I got the lecture from my supervisor yesterday. To paraphrase: "You're sitting on data. Publish it now!"
I guess there's a spectrum of people out there in research: those who publish fast and furious, and those who publish slowly and painstakingly. I guess I'm on the far end of the spectrum: I really like to make sure that the data I have is really right before pushing it out the door.
This particular data set was collected about a year and a half ago, back when 36-bp Illumina reads were all the rage, so yes, I've been sitting on it for a long time. However, if you read my notebooks, there's a clear evolution. Even in my database, the table names are marked as "tbl_run5_", so you can get the idea of how many times I've done this analysis. (I didn't start with the database on the first pass.)
At this point, and as of late last week (aka, Thursday), I'm finally convinced that my analysis of the data is reproducible, reliable and accurate - and I'm thrilled to get it down in a paper. I just look back at the lab book full of markings and have to wonder what would have happened if I'd have published earlier... bleh!
So, this is my own personal dilemma: How do you publish as quickly as possible without opening the door to making mistakes? I always err on the side of caution, but judging by what makes it out to publication, maybe that's not the right path. I've heard stories of people sending results they knew to be incorrect to the reviewers with the assumption that they could fix it up by the time the reviewers come back with comments..
Balancing publication quality, quantity and speed is probably another of those necessary skills that grad students will just magically pick up somewhere along the way towards getting a PhD. (Others in the list include teaching a class of 300 undergraduates, getting and keeping grants and starting up your own lab)
I think I'm going to spend a few minutes this afternoon (between writing paragraphs, maybe?) looking for a good grad school HOWTO. The few I've come across haven't dealt with this particular subject, but I'm sure it's out there somewhere.
I guess there's a spectrum of people out there in research: those who publish fast and furious, and those who publish slowly and painstakingly. I guess I'm on the far end of the spectrum: I really like to make sure that the data I have is really right before pushing it out the door.
This particular data set was collected about a year and a half ago, back when 36-bp Illumina reads were all the rage, so yes, I've been sitting on it for a long time. However, if you read my notebooks, there's a clear evolution. Even in my database, the table names are marked as "tbl_run5_
At this point, and as of late last week (aka, Thursday), I'm finally convinced that my analysis of the data is reproducible, reliable and accurate - and I'm thrilled to get it down in a paper. I just look back at the lab book full of markings and have to wonder what would have happened if I'd have published earlier... bleh!
So, this is my own personal dilemma: How do you publish as quickly as possible without opening the door to making mistakes? I always err on the side of caution, but judging by what makes it out to publication, maybe that's not the right path. I've heard stories of people sending results they knew to be incorrect to the reviewers with the assumption that they could fix it up by the time the reviewers come back with comments..
Balancing publication quality, quantity and speed is probably another of those necessary skills that grad students will just magically pick up somewhere along the way towards getting a PhD. (Others in the list include teaching a class of 300 undergraduates, getting and keeping grants and starting up your own lab)
I think I'm going to spend a few minutes this afternoon (between writing paragraphs, maybe?) looking for a good grad school HOWTO. The few I've come across haven't dealt with this particular subject, but I'm sure it's out there somewhere.
Labels: Grad School, rant
2 Comments:
Two good books
For the new graduate student:
At the Bench: A Laboratory Navigator
and for the new Prof:
At The Helm: A Laboratory Navigator
One test would be to write up what you have & then run it by someone who is willing to play reviewer (and ideally is distant enough to not have already have followed your experiment in detail). That might give you some idea of what issues reviewers might raise and potentially some you or your advisor hasn't thought of.
Vancouver->Boston is pretty distant :-)
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