Some Things Last A Long Time

How long does it take to publish a paper?

The answer is – in our experience, at least – about 9 months.

That’s right, it takes about the same amount of time to have a baby as it does to publish a scientific paper. Discussing how we can make the publication process quicker is for another day. Right now, let’s get into the numbers.

The graphic shows the time taken from submission-to-publication for papers on which I am an author. I’m missing data for two papers (one from 1999 and one from 2002) and the Biol Open paper is published online but not yet “in print”, but mostly the information is complete. If you want to calculate this for your own papers; my advice would be to keep a spreadsheet of submission and decision dates as you go along… and archive your emails.

In the last analysis, a few people pointed out ways that the graphic could be improved, and I’ve now implemented these changes.

The graphic shows that the journey to publication is in four eras:

  1. Pre-time (before 0 on the x-axis): this is the time from first submission to the first journal. A dark time which involves rejection.
  2. Submission at the final journal (starting at time 0). Again, the orange periods are when the manuscript is with the journal and the green, when it is with us. Needless to say this green time is mainly spent doing experimental work (compare green periods for reviews and for papers)
  3. Acceptance! This is where the orange bar stops. The manuscript is then readied for publication (blank area).
  4. Published online. A purple period that ends with final publication in print.

Note that: i) the delays are more-or-less negated by preprinting provided deposition is before the first submission (grey line, for Biol Open paper), ii) these delay diagrams do not take into account the original drafting/rewriting cycle before the fist submission – nor the time taken to do the work!

So… how long does it take to publish a paper?

In the top right graph: the time from first submission to being published online is 250 days on average (median). This is shown by the blue bar. If we throw in the average time it takes to go from online to print (15 days) this gives 265 days. The average time for human gestation is 266 days. So it takes about the same amount of time to have a baby as it does to publish a paper! By contrast, reviews take only 121 days, equivalent to four lunar cycles (118 days).

My 2005 paper at Nature holds the record for the most protracted publication 399 days from submission to publication. The fastest publication is the most recent, our Biol Open paper was online 49 days after submission (it was also online 1 day before submission as a preprint).

In the bottom right graph: I added together the total time each paper was either with the journal, or with us, and plotted the average. The time from acceptance-to-publication online is shown stacked onto the “time with journal” column. You can see from this graphic that the lion’s share of the delay comes from revisions that we must do in order for a paper to be published. Multiple revisions and submissions also push these numbers up compared to the totals for reviews.

How representative are these numbers?

This is a small dataset at many different journals and so it is difficult to conclude much. With this analysis, I was hoping to identify ‘slow journals’ that we should avoid and also to think about our publication strategy (as much as a crap shoot can have a strategy). The whole process is stochastic and I don’t see any reason to change the way that we navigate the system. Having said this, I can’t see us doing any more methods/book chapters, as they are just so slow.

Just over half of our papers have some “pre-time”, i.e. they got rejected from at least one other journal before finding a home. A colleague of mine likes to say:

“if your paper is accepted at the first journal you send it to, you sent it to the wrong place”

One thing for sure is that publication takes a long time. And I don’t think our experience is uncommon. The pace of scientific publishing has been described as glacial by Leslie Vosshall and I don’t disagree with this. I think the 9 months figure is probably representative for most areas of biology. I know that other scientists in my field, who have more tenacity for rejections and for slugging it out at high impact journals, have much longer times from 1st submission to acceptance. In my opinion, wasting even more time chasing publication is crazy, counter-productive and demotivating for the people in the lab.

The irony in all this is that, even though we are working at the absolute bleeding edge of science with all of this technology at our disposal, our methods for reporting science are badly out of date. And with that I’ll push the “publish” button and this will be online…

The title of this post comes from ‘Some Things Last A Long Time’ by Daniel Johnston from his LP ‘1990’.

I’m Gonna Crawl

Fans of data visualisation will know the work of Edward Tufte well. His book “The Visual Display of Quantitative Information” is a classic which covers the history and the principals of conveying data in a concise way, that is easy to interpret. He is also credited with two different dataviz techniques: sparklines and image quilts. It was these two innovations that came to mind when I was discussing some cell migration results generated in our lab.

Sparklines are small displays of 1D information versus time to highlight the profile (think: stocks and shares).

Image quilts are arrays of images that together quickly provide you with an overview (think: Google Images results).

Analysing cell migration generates ‘tracks’ of many cells as they move around a 2D surface. Tracks are pairs of XY co-ordinates at different time points. We want to understand how these tracks change if we do something to the cells, e.g. knock-down a particular protein. There are many ways to analyse this. Such as: looking at the speed of migration, their directionality, etc. etc. When we were looking at lots of tracks, all jumbled up, I thought of sparklines and of image quilts and thought the easiest way to compare a control and test group would be to generate something similar.

We start out with many tracks within a field:

 

overviewIt’s difficult to see what is happening here, so it needs to be simplified.

I wrote a couple of procedures in IgorPro that calculated the cumulative distance that each cell had migrated at a given time point (say, the end of the movie). These cumulative distances were then ranked and then the corresponding cells were arrayed in the x-dimension according to how far they migrated. This was a little bit tricky to do, but that’s another story.

 

This plot shows the tracks with the shortest/slowest to the left and the furthest/fastest to the right. This can then be compared to a test set and differences become apparent. However, we need to look at many tracks and expanding these “sparklines” further is not practical – we want to provide an overview.

Accordingly, I wrote another procedure to array them in an XY array with a given spacing between the start points. This should give an “image quilt” feel.

I added gridlines to indicate the start position. The result is that a nice overview is seen and differences between groups can be easily seen at first glance (or not seen if there is no effect!).

This method works well to compare control and test groups that have a similar number of cells. If N is different (say, more than 10%), we need to take a random sample of tracks and array those to get a feel for what’s happening. Obviously the tracks could be arrayed according whatever parameter is required, e.g. highest speed, most directional etc. etc.

One thought is to do a further iteration where the tracks are oriented so that the start and end points are at the same point in X, or oriented so that the tracks have the same starting trajectory. As it is, the mix of trajectories spoils the ease of interpretation.

Obviously, this can be applied to tracks of anything: growing and shrinking microtubules, endosome/lysosome movement etc. etc.

Any suggestions for improvements are welcome, but I think this is a quick and easy way to just eyeball the data to see if there are any differences before calculating any other parameters. I thought I’d put the idea out there – maybe together with the code if there is any interest.

The post title is from I’m Gonna Crawl – Led Zeppelin from their In Through The Out Door LP

My Blank Pages

Books about the MRC Laboratory of Molecular Biology are plentiful. If you haven’t read any, the best place to start are the books written by some of the Nobelists themselves: “I Wish I’d Made You Angry Earlier” by Perutz, “My Life in Science” by Brenner. Also, “Sequences, Sequence, Sequences” by Sanger, “What Mad Pursuit” by Crick and even Watson’s “The Double Helix” cover ‘how it was done’ and ‘what the place is like’. After that are the biographies of the Nobelists and their associates. Then comes the next layer, the comprehensive but rather dry “Designs for Life: Molecular Biology after World War II” by de Chadarevian and hell, even “The Eighth Day of Creation” by Judson is substantially about the LMB, since so many major discoveries in Molecular Biology happened there.

If your appetite is not sated after wading through all of those, then there are the books for the insiders.

John Finch wrote a book “A Nobel Fellow on Every Floor” which was enjoyable, if rather selective on who and what was included. The latest book from the LMB Press is a collection of essays entitled “Memories and Consequences: Visiting Scientists at the MRC Laboratory of Molecular Biology, Cambridge”. It was edited by Hugh Huxley and was made available last summer (around the time of his death).
You can get it here

 

memories

The premise of Memories and Consequences is that there were a large number of postdoctoral fellows, mainly from the USA, who spent time at the LMB (in the 60s, mainly) and then went away and had hugely successful scientific careers. At one point in the book, Tom Steitz writes that, of his friends during this period, 40% are now NAS members! The essays cover the time of these visitors in England and how it shaped their subsequent careers.

This is definitely a book to dip in and out of. The experiences are actually pretty repetitive: yes, we drive on the other side of the road; Cambridge is a very stuffy place and Max Perutz liked to be called Max. This repetition is amplified if the chapters are read one-after-the-other. Overall however, the essays are nice reminiscences of a booming time in Molecular Biology and many capture the magic of working at the LMB during this period. Brenner and Crick come to life and even Sir Lawrence Bragg looms large in many chapters filling the authors with awe.

When I first downloaded the book, I read the chapters by those whose work I am most familiar. I didn’t even know that Dick McIntosh had spent not one but two sabbaticals at the LMB. Tom Pollard, Harvey Lodish etc. followed. I then read the other chapters when I had more time.

The best chapters were those by Harry Noller and by Peter Moore who gave the right amount (for my taste) of personal insight to their stay at the LMB. I would recommend that the reader skips the chapter by William Dove and Alexandra Shevlovsky, who tried to be a bit clever and didn’t quite pull it off. Sid Altman’s chapter has previously been published and I actually witnessed him read this out (more-or-less) verbatim at the DNA50+1 celebrations – which was far more enjoyable than it sounds.

In short, I enjoyed the book and it’s worth reading some of the chapters if you have a leaning towards the history of science, but there are plenty of other books (listed above) where you should start if you want to find out what life is like inside the Nobel Prize Factory.

I’ll leave you with three quotes that I enjoyed immensely:

“I remember seeing copies of the journal Cell, where we all yearned to publish (though, I noticed, not the really great scientists, like John Sulston or Sydney Brenner). I would shudder and turn away; Cell was for other scientists, not for me.”
Cynthia Kenyon

“Like many others who worked at the LMB in that era, I still think of its modus operandi as exemplifying the blueprint that all scientific research establishments should aspire to emulate. Pack the very best scientists you can find into a building, so densely that they cannot avoid talking to each other, and encourage them to interact in every other way you can. A canteen or dining room might be a good idea. (The facility itself need not be luxurious, and indeed, it is probably better if it is not.) Give those scientists ample staff support, and all the money they need to get on with the job. Stir well, and then be patient because good science takes time. My subsequent career has taught me that this recipe is much harder to execute than it is to describe. I still wonder how the MRC managed to do it so well for so long.”
Peter Moore

“I learned that protein chemistry didn’t need me, that King’s College High Table was for tougher folk than I, and that Sydney talked but Francis conversed.”
Frank Stahl

A comprehensive guide to LMB books is available here

Don’t worry, book reviews will be a very infrequent feature as I hardly have any time to read books these days!

The post title is from My Blank Pages – Velvet Crush from their LP Teenage Symphonies to God. Presumably a play on the Dylan/Byrds song My Back Pages.

All Together Now

In the lab we use IgorPro from Wavemetrics for analysis. Here is a useful procedure to plot all XY pairs in an experiment. I was plotting out some cell tracking data with a colleague and I knew that I had this useful function buried in an experiment somewhere. I eventually found it and thought I’d post it here. I’ll add it to the code section of the website soon. Looking at it, it doesn’t look like it was written by me. A search of IgorExchange didn’t reveal its author, so maybe it was me. Apologies if it wasn’t.

The point is: if you have a bunch of XY pairs and you just want to plot all of them in one window to look at them. If they are 2D waves or a small number of 1D waves, this is straightforward. If you have hundreds, you need a function!

An example would be fluorescence recordings versus time (where each time wave is unique to the fluorescence trace) or XY co-ordinates of a particle in space.

To use this procedure, you need an experiment with a logical naming system for 1D waves. something like X_ctrl1, X_ctrl2, X_ctrl3 etc. and Y_ctrl1, Y_ctrl2, Y_ctrl3 etc. Paste the following into the Procedure Window (command+m).


Function PlotAllWaves(theYList,theXlist)
	String theYList
	String theXList
 	display
	Variable i=0
	string aWaveName = ""
	string bWaveName = ""
	do
		aWaveName = StringFromList(i, theYList)
		bWavename = StringFromList(i, theXList)
		WAVE/Z aWave = $aWaveName
		WAVE/Z bWave = $bWaveName
		if (!WaveExists(aWave))
			break
		endif
 		appendtograph aWave vs bWave
		i += 1
	while(1)
End

After compiling you can call the function by typing in the Command Window:


PlotAllWaves(wavelist("x_*", ";", ""),wavelist("y_*", ";", ""))

You’ll need to change this for whatever convention you are using for your wave naming system. You will know how to do this if you have got this far!

This function is very useful for just eyeballing the data after you have imported it. The databrowser shows only one wave at a time, but it is preferable to look at all the waves to find errors, spot outliers or trends etc.

Edit 28/4/15: the logical naming system and the order in which the waves were added to the experiment are crucial for this to work. We’re now using two different versions of this code that either a) check that the waves are compatible or b) concatenate the waves into a 2D wave before plotting. This reduces errors in plotting.

The post title is taken from All Together Now – The Beatles from the Yellow Submarine soundtrack.

A Day In The Life

#paperoftheday #potd

A common complaint from other PIs is that they “don’t read enough any more”. I feel like this too and a solution was proposed by a friend of a friend*: try to read one paper per day.

This seemed like a good idea and I started to do this in 2013. The rules, obviously, can be set by you. Here’s my version:

  1. Read one paper each working day.
  2. If I am away, or reviewing a paper for a journal or colleague, then I get a pass.
  3. Read it sufficiently to be able to explain it to somebody else, i.e. don’t just scan the abstract and look at the figures. Really read it and understand it. Scan and skim as many other papers as you normally would!
  4. Only papers reporting primary research count towards #paperoftheday.
  5. If it was really good or worth telling people about – tweet about it.
  6. Make a simple database in Excel or Papers – this helps you keep track, make notes about the paper (to see if you meet #3) and allows you to find the paper easily in the future (this last point turned out to be very useful).

I started this in 2013 (for one full year) and am trying to continue in 2014. I feel that this is succeeding in making me read more than I would have otherwise done.

My stats for 2013 were:

  • 85% success rate. Filling that last 15% will be tough.
  • Stats errors in 48% of papers! Most common error was incorrect use of Student’s  t-test.
  • 68% of papers were from 2013 and 22% were from 2009-2012.

The big surprise was which journals I read most:

  1. J Cell Biol 13
  2. PLOS One 12
  3. Nat Cell Biol 10
  4. PNAS 10
  5. Curr Biol 9
  6. Mol Biol Cell 8
  7. Nature 8
  8. Dev Cell 7
  9. eLife 7
  10. Nature Methods 7
  11. Cell 6
  12. Neuron 6
  13. Traffic 6
  14. J Cell Sci 4
  15. Science 4

I thought that Cell would be much higher and PNAS would be much lower. Since where we publish is dictated by who is likely to see and read the paper, this list was thought-provoking.

*I think this was a colleague of @david_s_bristol who suggested it, sometime in 2012.

The post title is of course from A Day in The Life – The Beatles from the LP Sgt Pepper’s Lonely Hearts Club Band. For the first line…

So Long

How long does it take to publish a paper?

I posted the picture below on Twitter to show how long it takes for us to publish a paper.

papers

The answer is 235 days. This is the median time from submission at the first journal to publication online or in print. The data are from our last ten papers.

The infographic proved popular with 40 retweets and 22 favourites. It was pointed out to me that the a few things would improve this visualisation:

1. Showing the names of the journals

2. Showing when the 1st submission was relative to the 1st submission at the journal that finally accepted the paper

3. What about reviews and other types of publication.

I am working on updating the graph to show all of these things… watch this space.

My point was really to show (perhaps to non-scientists) how long the process of publishing a paper can be. There is other information that can be gleaned from this, e.g. what proportion of time is at the journal’s side and how much is at our end?

The people who are eager to see which journals perform badly (slowly) will be disappointed: this is a very small subset of papers from one lab. I’d be interested in scraping the information on journal tardiness on a larger scale and synthesising this so that it can inform journal choice. Recently though major publishers have taken steps to make this information less accessible so don’t hold your breath.

The title of this post is from So Long by Cian Ciarán from the LP ‘Outside In’

Counting backwards

I thought I would start add a blog to our lab website. The plan is to update maybe once a week with content that is too long for twitter but doesn’t fit in the categories on the lab website. I’m thinking extra analysis, paper commentaries, outreach activities etc. Let’s see how it goes.

First up: how do you count the number of words or characters in a text file?

Microsoft Word has a nice feature for doing this, but poor old TextEdit does not. Fortunately, AppleScript can come to the rescue! I found a script on the web to count the number of words in a TextEdit file and modified it slightly to give the number of characters as well.

Why would you want to do this? When editing fields on a web form (particularly grant application forms) it’s not practical to do this in the browser and these fields often have strict limits on words and characters.

Here is the code:


tell application "TextEdit"
	set wc to count words of document 1
	set cc to count characters of document 1
	if wc is equal to 1 then
		set txt to " word, "
	else
		set txt to " words, "
	end if
	if cc is equal to 1 then
		set txtc to " character."
	else
		set txtc to " characters."
	end if

	set result to "This text comprises " & (wc as string) & txt & (cc as string) & txtc
	display dialog result with title "WordStats" buttons {"OK"} default button "OK"
end tell

If you are new to this: open AppleScript Editor. New file. Paste in the code above. Click Compile. It should look something like this:

textedit

 

Now Save it to your Scripts folder in home/Library. Call it something sensible e.g. TextEditCounter. Now, in AppleScript Editor. Click Preferences and check the box ‘Show script menu in menu bar’. This shows the AppleScript icon in your menu bar and if you click there, you should see your script there waiting for you to use it.

This blog title is taken from Counting Backwards – Throwing Muses from their LP The Real Ramona.