Tag: fast journals


Sunday Data/Statistics Link Roundup (9/2/2012)

  1. Just got back from IBC 2012 in Kobe Japan. I was in an awesome session (organized by the inimitable Lieven Clement) with great talks by Matt McCall, Djork-Arne Clevert, Adetayo Kasim, and Willem Talloen. Willem’s talk nicely tied in our work and how it plays into the pharmaceutical development process and the bigger theme of big data. On the way home through SFO I saw this hanging in the airport. A fitting welcome back to the states. Although, as we talked about in our first podcast, I wonder how long the Big Data hype will last…
  2. Simina B. sent this link along for a masters program in analytics at NC State. Interesting because it looks a lot like a masters in statistics program, but with a heavier emphasis on data collection/data management. I wonder what role the stat department down there is playing in this program and if we will see more like it pop up? Or if programs like this with more data management will be run by stats departments other places. Maybe our friends down in Raleigh have some thoughts for us. 
  3. If one set of weekly links isn’t enough to fill your procrastination quota, go check out NextGenSeek’s weekly stories. A bit genomics focused, but lots of cool data/statistics links in there too. Love the “extreme Venn diagrams”. 
  4. This seems almost like the fast statistics journal I proposed earlier. Can’t seem to access the first issue/editorial board either. Doesn’t look like it is open access, so it’s still not perfect. But I love the sentiment of fast/single round review. We can do better though. I think Yihue X. has some really interesting ideas on how. 
  5. My wife taught for a year at Grinnell in Iowa and loved it there. They just released this cool data set with a bunch of information about the college. If all colleges did this, we could really dig in and learn a lot about the American secondary education system (link via Hilary M.). 
  6. From the way-back machine, a rant from Rafa about meetings. Stayed tuned this week for some Simply Statistics data about our first year on the series of tubes

What is a major revision?

I posted a little while ago on a proposal for a fast statistics journal. It generated a bunch of comments and even a really nice follow up post with some great ideas. Since then I’ve gotten reviews back on a couple of papers and I think I realized one of the key issues that is driving me nuts about the current publishing model. It boils down to one simple question: 

What is a major revision? 

I often get reviews back that suggest “major revisions” in one or many of the following categories:

  1. More/different simulations
  2. New simulations
  3. Re-organization of content
  4. Re-writing language
  5. Asking for more references
  6. Asking me to include a new method
  7. Asking me to implement someone else’s method for comparison
I don’t consider any of these major revisions. Personally, I have stopped asking for them as major revisions. In my opinion, major revisions should be reserved for issues with the manuscript that suggest that it may be reporting incorrect results. Examples include:
  1. No simulations
  2. No real data
  3. The math/computations look incorrect
  4. The software didn’t work when I tried it
  5. The methods/algorithms are unreadable and can’t be followed
The first list is actually a list of minor/non-essential revisions in my opinion. They may improve my paper, but they won’t confirm that it is correct or not. I find that they are often subjective and are up to the whims of referees. In my own personal refereeing I am making an effort to remove subjective major revisions and only include issues that are critical to evaluate the correctness of a manuscript. I also try to divorce the issues of whether an idea is interesting or not from whether an idea is correct or not. 
I’d be curious to know what other peoples’ definitions of major/minor revisions are?