Bioinformatics journals: time from submission to acceptance, revisited

Before we start: yes, we’ve been here before. There was the Biostars question “Calculating Time From Submission To Publication / Degree Of Burden In Submitting A Paper.” That gave rise to Pierre’s excellent blog post and code + data on Figshare.

So why are we here again? 1. It’s been a couple of years. 2. This is the R (+ Ruby) version. 3. It’s always worth highlighting how the poor state of publicly-available data prevents us from doing what we’d like to do. In this case the interesting question “which bioinformatics journal should I submit to for rapid publication?” becomes “here’s an incomplete analysis using questionable data regarding publication dates.”

Let’s get it out of the way then.
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“Advance” access and DOIs: what’s the problem?

A DOI, this morning

A DOI, this morning

When I arrive at work, the first task for the day is “check feeds”. If I’m lucky, in the “journal TOCs” category, there will be an abstract that looks interesting, like this one on the left (click for larger version).

Sometimes, the title is a direct link to the article at the journal website. Often though, the link is a Digital Object Identifier or DOI. Frequently, when the article is labelled as “advance access” or “early”, clicking on the DOI link leads to a page like the one below on the right.

DOI #fail

DOI #fail

In the grand scheme of things I suppose this rates as “minor annoyance”; it means that I have to visit the journal website and search for the article in question. The question is: why does this happen? I’m not familiar with the practical details of setting up a DOI, but I assume that the journal submits article URLs to the DOI system for processing. So who do I blame – journals, for making URLs public before the DOI is ready, or the DOI system, for not processing new URLs quickly enough?

There’s also the issue of whether terms like “advance access” have any meaning in the era of instant, online publishing but that’s for another day.

Career advice: switching to computational research

Laboratory work, of the “wet” kind, not working out for you? Or perhaps you just need new challenges. Think you have some aptitude with data analysis, computers, mathematics, statistics? Maybe a switch to computational biology is what you need.

That’s the topic of the Nature Careers feature “Computing: Out of the hood“. With thoughts and advice from (on Twitter) @caseybergman, @sarahmhird, @kcranstn, @PavelTomancak, @ctitusbrown and myself.

I enjoyed talking with Roberta and she did a good job of capturing our thoughts for the article. One of these days, I might even write here about my own journey in more detail.

We really don’t care what statistical method you used

Update: as pointed out in the comments, the amusing error in this article has been “corrected” (or at least, “edited away”). Thanks for your interest.
Update: I note that this article is now “Highly Accessed” ;)

An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver
BMC Systems Biology 2011, 5(Suppl 3):S4

(insert statistical method here). No, really.

With thanks to Simon J Greenhill and Dave Winter.

Can a journal make a difference? Let’s find out.

Academic journals. Frankly, I’m not a big fan of any of them. There are too many. They cost too much. Much of what they publish is inconsequential, read by practically no-one or just downright incorrect. Much of the rest is badly-written and boring. The people who publish them have an over-inflated sense of their own importance. They’re hidden behind paywalls. And governed by ludicrous metrics. The system by which articles are accepted or rejected is arcane and ridiculous. I mean, I could go on…

No, what really troubles me about journals is that they only tell a very small part of the story – the flashy, attention-grabbing part called “results”. We learn from high school onwards that a methods section should be sufficient for anyone to reproduce the results. This is one of the great lies of science. Go read any journal in your field and give it a try. It’s even the case in computation, an area which you might think less prone to the problems in reproducing wet-lab science (“the Milli-Q must have been off”).

We have this wonderful thing called the Web now. The Web doesn’t have a page limit, so you can describe things in as much detail as you wish. Better still, you can just post your methods and data there in full, for all to see, download and reproduce to their hearts content. You’d like some credit for doing that though, right?

So if you do research – any kind of research – that involves computation, your code is open-source, reusable, well-documented and robust (think: tests) and you want to share it with the world, head over to a new journal called BMC Open Research Computation, which is now open for submissions. Your friendly team of enlightened editors awaits.

More information at Science in the Open and Saaien Tist. Full disclosure: I’m on the editorial board of this journal and was invited to write a launch post.