Although I started blogging in 2012, 2013 has been my first year of blogging. It’s been fun so far if a bit sporadic. I’ve posted approximately once a week, which is a bit less than I’d like. And I’ve had some fun. My top posts for the year are listed in order below. Looking forward to continuing to blog, and improve, in the coming year and beyond. My blog resolution for 2014 is to post more frequently but to also work on a few posts that are more like mini-papers, studies of actual data that’s interesting to the scientific community similar to my analysis of review times in journals. (Caveat: this ranking is based on absolute numbers so short-changes more recent posts that haven’t had as much time to be viewed. But really I think it’s pretty reasonable)
I had some failures in 2013 too. Some posts that I was sure would knock it out of the park, but didn’t garner much interest. Also, I started a series (parts 1, 2, 3, 4, 5, 6) that was supposed to chronicle my progress on a computational biology project in real time. That series has stalled because it was a bit harder to put together the project than I thought it would be (this is not surprising in the least BTW) and I ran into other more pressing things I needed to do. I’m still planning on finishing this- it seems like a perfect project for the Jan-Feb lull that sometimes occurs.
Top Posts of 2013 for The Mad Scientist Confectioner’s Club
- Scientific paper easter eggs: Far and away my most viewed post. A list of funny things that authors have hidden in scientific papers, but also of just funny (intentionally or not) scientific papers. And these keep coming too- so much so that I started a Tumblr to add new ones.
- How long is long: Time in review for scientific publications/Time to review for scientific publications revisited: These two posts have analysis I’ve done of the time my papers spent in review. After some Twitter discussions I posted the second one that looked at how long the papers took to get their first review returned, which is more fair to the journals (my first post looked at overall time, including the time that I spent revising the papers). Look for a continuation of this in 2014, hopefully including contribution of data from other people.
- Eight red flags in bioinformatics analyses: I’m still working on revising this post into a full paper since I think there’s a lot of good stuff in there. Unfortunately on the back burner right now. However, I did get my first Nature publication (in the form of a Comment) out of the deal. Not bad.
- Reviewer 3, I presume?: This post was to recap the (moderate) success of a Tweet I made, and the turning of that Tweet into a sweet T-shirt!
- Gaming the system: How to get an astronomical h-index with little scientific impact: One of my favorite posts (though I think I wrote it in 2012) does a bit of impact analysis on a Japanese bioinformatic group that published (and still publishes) a whole bunch of boilerplate papers- and got an h-index close to 50!
- How can two be worse than one? Replicates in high-throughput experiments: I’m including this one so that this list isn’t 5 long, and also because I like this post. This is essentially a complaint about the differences between the way that statisticians and data analysts (computational biologists, e.g.) see replicates in high-throughput data and how wet-lab biologists see them. It has yielded one of my new favorite quotes (from myself) that’s not actually in the post: “The only reason to do an experiment with two replicates because you know replicates are important, but you don’t know why.”
Have a great New Year and see everyone in 2014!