Category Archives: publishing

Parallel lines: new paper on modelling mitotic microtubules in 3D

We have a new paper out! You can access it here.

The people

This paper really was a team effort. Faye Nixon and Tom Honnor are joint-first authors. Faye did most of the experimental work in the final months of her PhD and Tom came up with the idea for the mathematical modelling and helped to rewrite our analysis method in R. Other people helped in lots of ways. George did extra segmentation, rendering and movie making. Nick helped during the revisions of the paper. Ali helped to image samples… the list is quite long.

The paper in a nutshell

We used a 3D imaging technique called SBF-SEM to see microtubules in dividing cells, then used computers to describe their organisation.

What’s SBF-SEM?

Serial block face scanning electron microscopy. This method allows us to take an image of a cell and then remove a tiny slice, take another image and so on. We then have a pile of images which covers the entire cell. Next we need to put them back together and make some sense of them.

How do you do that?

We use a computer to track where all the microtubules are in the cell. In dividing cells – in mitosis – the microtubules are in the form of a mitotic spindle. This is a machine that the cell builds to share the chromosomes to the two new cells. It’s very important that this process goes right. If it fails, mistakes can lead to diseases such as cancer. Before we started, it wasn’t known whether SBF-SEM had the power to see microtubules, but we show in this paper that it is possible.

We can see lots of other cool things inside the cell too like chromosomes, kinetochores, mitochondria, membranes. We made many interesting observations in the paper, although the focus was on the microtubules.

So you can see all the microtubules, what’s interesting about that?

The interesting thing is that our resolution is really good, and is at a large scale. This means we can determine the direction of all the microtubules in the spindle and use this for understanding how well the microtubules are organised. Previous work had suggested that proteins whose expression is altered in cancer cause changes in the organisation of spindle microtubules. Our computational methods allowed us to test these ideas for the first time.

Resolution at a large scale, what does that mean?

The spindle is made of thousands of microtubules. With a normal light microscope, we can see the spindle but we can’t tell individual microtubules apart. There are improvements in light microscopy (called super-resolution) but even with those improvements, right in the body of the spindle it is still not possible to resolve individual microtubules. SBF-SEM can do this. It doesn’t have the best resolution available though. A method called Electron Tomography has much higher resolution. However, to image microtubules at this large scale (meaning for one whole spindle), it would take months or years of effort! SBF-SEM takes a few hours. Our resolution is better than light microscopy, worse than electron tomography, but because we can see the whole spindle and image more samples, it has huge benefits.

What mathematical modelling did you do?

Cells are beautiful things but they are far from perfect. The microtubules in a mitotic spindle follow a pattern, but don’t do so exactly. So what we did was to create a “virtual spindle” where each microtubule had been made perfect. It was a bit like “photoshopping” the cell. Instead of straightening the noses of actresses, we corrected the path of every microtubule. How much photoshopping was needed told us how imperfect the microtubule’s direction was. This measure – which was a readout of microtubule “wonkiness” – could be done on thousands of microtubules and tell us whether cancer-associated proteins really cause the microtubules to lose organisation.

The publication process

The paper is published in Journal of Cell Science and it was a great experience. Last November, we put up a preprint on this work and left it up for a few weeks. We got some great feedback and modified the paper a bit before submitting it to a journal. One reviewer gave us a long list of useful comments that we needed to address. However, the other two reviewers didn’t think our paper was a big enough breakthrough for that journal. Our paper was rejected*. This can happen sometimes and it is frustrating as an author because it is difficult for anybody to judge which papers will go on to make an impact and which ones won’t. One of the two reviewers thought that because the resolution of SBF-SEM is lower than electron tomography, our paper was not good enough. The other one thought that because SBF-SEM will not surpass light microscopy as an imaging method (really!**) and because EM cannot be done live (the cells have to be fixed), it was not enough of a breakthrough. As I explained above, the power is that SBF-SEM is between these two methods. Somehow, the referees weren’t convinced. We did some more work, revised the paper, and sent it to J Cell Sci.

J Cell Sci is a great journal which is published by Company of Biologists, a not-for-profit organisation who put a lot of money back into cell biology in the UK. They are preprint friendly, they allow the submission of papers in any format, and most importantly, they have a fast-track*** option. This allowed me to send on the reviews we had and including our response to them. They sent the paper back to the reviewer who had a list of useful comments and they were happy with the changes we made. It was accepted just 18 days after we sent it in and it was online 8 days later. I’m really pleased with the whole publishing experience with J Cell Sci.


* I’m writing about this because we all have papers rejected. There’s no shame in that at all. Moreover, it’s obvious from the dates on the preprint and on the JCS paper that our manuscript was rejected from another journal first.

** Anyone who knows something about microscopy will find this amusing and/or ridiculous.

*** Fast-track is offered by lots of journals nowadays. It allows authors to send in a paper that has been reviewed elsewhere with the peer review file. How the paper has been revised in light of those comments is assessed by at the Editor and one peer reviewer.

Parallel lines is of course the title of the seminal Blondie LP. I have used this title before for a blog post, but it matches the topic so well.

Tips from the blog XI: Overleaf

I was recently an external examiner for a PhD viva in Cambridge. As we were wrapping up, I asked “if you were to do it all again, what would you do differently?”. It’s one of my stock questions and normally the candidate says “oh I’d do it so much quicker!” or something similar. However, this time I got a surprise. “I would write my thesis in LaTeX!”, was the reply.

As a recent convert to LaTeX I could see where she was coming from. The last couple of manuscripts I have written were done in Overleaf and have been a breeze. This post is my summary of the site.


I have written ~40 manuscripts and countless other documents using Microsoft Word for Mac, with EndNote as a reference manager (although I have had some failed attempts to break free of that). I’d tried and failed to start using TeX last year, motivated by seeing nicely formatted preprints appearing online. A few months ago I had a new manuscript to write with a significant mathematical modelling component and I realised that now was the chance to make the switch. Not least because my collaborator said “if we are going to write this paper in Word, I wouldn’t know where to start”.

screen-shot-2016-12-11-at-07-39-13I signed up for an Overleaf account. For those that don’t know, Overleaf is an online TeX writing tool on one half of the screen and a rendered version of your manuscript on the other. The learning curve is quite shallow if you are used to any kind of programming or markup. There are many examples on the site and finding out how to do stuff is quick thanks to LaTeX wikibooks and stackexchange.

Beyond the TeX, the experience of writing a manuscript in Overleaf is very similar to editing a blog post in WordPress.


The best thing about Overleaf is the ability to collaborate easily. You can send a link to a collaborator and then work on it together. Using Word in this way can be done with DropBox, but versioning and track changes often cause more problems than it’s worth and most people still email Word versions to each other, which is a nightmare. Overleaf changes this by having a simple interface that can be accessed by multiple people. I have never used Google docs for writing papers, but this does offer the same functionality.

All projects are private by default, but you can put your document up on the site if you want to. You might want to do this if you have developed an example document in a certain style.



Depending on the type of account you have, you can roll back changes. It is possible to ‘save’ versions, so if you get to a first draft and want to send it round for comment, you can save a version and then use this to go back to, if required. This is a handy insurance in case somebody comes in to edit the document and breaks something.

You can download a PDF at any point, or for that matter take all the files away as a zip. No more finalfinalpaper3final.docx…

If you’re keeping score, that’s Overleaf 2, Word nil.


Placing figures in the text is easy and all major formats are supported. What is particularly nice is that I can generate figures in an Igor layout and output directly to PDF and put that into Overleaf. In Word, the placement of figures can be fiddly. Everyone knows the sensation of moving a picture slightly and it disappears inexplicably onto another page. LaTeX will put the figure in where you want it or the next best place. It just works.


This is what LaTeX excels at. Microsoft Word has an equation editor which has varied over the years from terrible to just-about-usable. The current version actually uses elements of TeX (I think). The support for mathematical text in LaTeX is amazing, not surprising since this is the way that most papers in maths are written. Any biologist will find their needs met here.

Templates and formatting

There are lots of templates available on Overleaf and many more on the web. For example, there are nice PNAS and PLoS formats as well as others for theses and for CVs and other documents. The typesetting is beautiful. Setting out sections/subsections and table of contents is easy. To be fair to Word, if you know how to use it properly, this is easy too, but the problem is that most people don’t, and also styles can get messed up too easily.


This works by adding a bibtex file to your project. You can do this with any reference manager. Because I have a huge EndNote database, I used this initially. Another manuscript I’ve been working on, my student started out with a Mendeley library and we’ve used that. It’s very flexible. Slightly more fiddly than with Word and EndNote. However, I’ve had so many problems (and crashes) with that combination over the years that any alternative is a relief.


You can set the view on the right to compile automatically or you can force updates manually. Either way the document must compile. If you have made a mistake, it will complain and try to guess what you have done wrong and tell you. Errors that prevent the document from being compiled are red. Less serious errors are yellow and allow compilation to go ahead. This can be slow going at first, but I found that I was soon up to speed with editing.


This is the name of the stuff at the header of a TeX document. You can add in all kinds of packages to cover proper usage of units (siunitx) or chemical notation (mhchem). They all have great documentation. All the basics, e.g. referencing, are included in Overleaf by default.


The entire concept of Overleaf is to work online. Otherwise you could just use TeXshop or some other program. But how about times when you don’t have internet access? I was concerned about this at the start, but I found that in practice, these days, times when you don’t have a connection are very few and far between. However, I was recently travelling and wanted to work on an Overleaf manuscript on the aeroplane. Of course, with Word, this is straightforward.

With Overleaf it is possible. You can do two things. The first is to download your files ahead of your period of internet outage. You can edit your main.tex document in an editor of your choice. The second option is more sophisticated. You can clone your project with git and then work on that local clone. The instructions of how to do that are here (the instructions, from 2015, say it’s in beta, but it’s fully working). You can work on your document locally and then push changes back to Overleaf when you have access once more.


OK. Nothing is perfect and I noticed that typos and grammatical errors are more difficult for me to detect in Overleaf. I think this is because I am conditioned with years of Word use. The dictionary is smaller than in Word and it doesn’t try to correct your grammar like word does (although this is probably a good thing!). Maybe I should try the rich text view and see if that helps. I guess the other downside is that the other authors need to know TeX rather than Word. As described above if you are writing with a mathematician, this is not a problem. For biologists though this could be a challenge.

Back to the PhD exam

I actually think that writing a thesis is probably a once-in-a-lifetime chance to understand how Microsoft Word (and EndNote) really works. The candidate explained that she didn’t trust Word enough to do everything right, so her thesis was made of several different documents that were fudged to look like one long thesis. I don’t think this is that unusual. She explained that she had used Word because her supervisor could only use Word and she had wanted to take advantage of the Review tools. Her heart had sunk when her supervisor simply printed out drafts and commented using a red pen, meaning that she could have done it all in LaTeX and it would have been fine.


I have been totally won over by Overleaf. It beats Microsoft Word in so many ways… I’ll stick to Word for grant applications and other non-manuscript documents, but I’m going to keep using it for manuscripts, with the exception of papers written with people who will only use Word.

The Arcane Model

I’m currently writing two manuscripts that each have a substantial data modelling component. Some of our previous papers have included computer code, but it was straightforward enough to have the code as a supplementary file or in a GitHub repo and leave it at that. Now with more substantial computation in the manuscript, I was wondering how best to describe it. How much detail is required?

How much explanation should be in the main text, how much is in supplementary information and how much is simply via commenting in the code itself?

I asked for recommendations for excellent cell biology papers that had a modelling component, where the computation was well described.

I got many replies and I’ve collated this list of papers below so that I can refer to them and in case it is useful for anyone who is also looking for inspiration. I’ve added the journal names only so that you can see what journals are interested in publishing cell biology with a computational component. Here they are, in no particular order:

  • This paper on modelling kinetochore-microtubule attachment in pombe. Published in JCB there is also a GitHub repo for the software, kt_simul written in Python. The authors used commenting and also put a PDF of the heavy detail on GitHub.
  • Modelling of signalling networks here in PLoS Comput Biol.
  • This paper using Voronoi tesselations to examine tissue packing of cells in EMBO J.
  • Two papers, this one in JCB featuring modelling of DNA repair and this one in Curr Biol on photoreceptors in flies.
  • Cell movements via depletion of chemoattractants in PLos Biol.
  • Protein liquid droplets as organising centres for biochemical reactions is a hot topic. This paper in Cell was recommended.
  • Final tip was to look at PLoS Comput Biol for inspiration, searching for cell biology topics. Papers like this one on Smoldyn 2.1.

Thanks to Hadrien Mary, Robert Insall, Joachim Goedhart, Stephen Floor, Jon Humphries, Luis Escudero, and Neil Saunders for the suggestions.

The post title is taken from “The Arcane Model” by The Delgados from their album Peloton.

Everything In Its Right Place

Something that has driven me nuts for a while is the bug in FIJI/ImageJ when making montages of image stacks. This post is about a solution to this problem.

What’s a montage?

You have a stack of images and you want to array them in m rows by n columns. This is useful for showing a gallery of each frame in a movie or to separate the channels in a multichannel image.

What’s the bug/feature in ImageJ?

If you select Image>Stacks>Make Montage… you can specify how you want to layout your montage. You can specify a “border” for this. Let’s say we have a stack of 12 images that are 300 x 300 pixels. Let’s arrange them into 3 rows and 4 columns with 0 border.

Screen Shot 2016-07-06 at 14.21.53So far so good. We have an image that is 1200 x 900. But it looks a bit rubbish, we need some grouting (white pixel space between the images). We don’t need a border, but let’s ignore that for the moment. So the only way to do this in ImageJ is to specify a border of 8 pixels.

Screen Shot 2016-07-06 at 14.22.27Looks a lot better. Ok there’s a border around the outside, which is no use, but it looks good. But wait a minute! Check out the size of the image (1204 x 904). This is only 4 pixels bigger in x and y, yet we added all that grouting, what’s going on?

The montage is not pixel perfect.

Screen Shot 2016-07-06 at 14.23.38

So the first image is not 300 x 300 any more. It is 288 x 288. Hmmm, maybe we can live with losing some data… but what’s this?

Screen Shot 2016-07-06 at 14.24.49

The next image in the row is not even square! It’s 292 x 288. How much this annoys you will depend on how much you like things being correct… The way I see it, this is science, if we don’t look after the details, who will? If I start with 300 x 300 images, it’s not too much to ask to end up with 300 x 300 images, is it? I needed to fix this.


I searched for a while for a solution. It had clearly bothered other people in the past, but I guess people just found their own workaround.

ImageJ solution for multichannel array

So for a multichannel image, where the grayscale images are arrayed next to the merge, I wrote something in ImageJ to handle this. These macros are available here. There is a macro for doing the separation and arraying. Then there is a macro to combine these into a bigger figure.

Igor solution

For the exact case described above, where large stacks need to be tiled out into and m x n array, I have to admit I struggled to write something for ImageJ and instead wrote something for IgorPRO. Specifying 3 rows, 4 columns and a grout of 8 pixels gives the correct TIFF 1224 x 916, with each frame showing in full and square. The code is available here, it works for 8 bit greyscale and RGB images.

Screen Shot 2016-07-06 at 14.55.31

I might update the code at some point to make sure it can handle all data types and to allow labelling and adding of a scale bar etc.

The post title is taken from “Everything In Its Right Place” by Radiohead from album Kid A.

Voice Your Opinion: Editors shopping for preprints is the future

Today I saw a tweet from Manuel Théry (an Associate Ed at Mol Biol Cell). Which said that he heard that the Editor-in-Chief of MBoC, David Drubin shops for interesting preprints on bioRxiv to encourage the authors to submit to MBoC. This is not a surprise to me. I’ve read that authors of preprints on bioRxiv have been approached by journal Editors previously (here and here, there are many more). I’m pleased that David is forward-thinking and that MBoC are doing this actively.

I think this is the future.

Why? If we ignore for a moment the “far future” which may involve the destruction of most journals, leaving a preprint server and a handful of subject-specific websites which hunt down and feature content from the server and co-ordinate discussions and overviews of current trends… I actually think this is a good idea for the “immediate future” of science and science publishing. Two reasons spring to mind.

  1. Journals would be crazy to miss out: The manuscripts that I am seeing on bioRxiv are not stuff that’s been dumped there with no chance of “real publication”. This stuff is high profile. I mean that in the following sense: the work in my field that has been posted is generally interesting, it is from labs that do great science, and it is as good as work in any journal (obviously). For some reason I have found myself following what is being deposited here more closely than at any real journal. Journals would be crazy to miss out on this stuff.
  2. Levelling the playing field: For better-or-worse papers are judged on where they are published. The thing that bothers me most about this is that manuscripts are only submitted to 1 or more journals before “finding their home”. This process is highly noisy and it means that if we accept that there is a journal hierarchy, your paper may or may not be deserving of the kudos it receives in its resting place. If all journals actively scour the preprint server(s), the authors can then pick the “highest bidder”. This would make things fairer in the sense that all journals in the hierarchy had a chance to consider the paper and its resting place may actually reflect its true quality.

I don’t often post opinions here, but I thought this would take more than 140 characters to explain. If you agree or disagree, feel free to leave a comment!

Edit @ 11:46 16-05-26 Pedro Beltrao pointed out that this idea is not new, a post of his from 2007.

Edit 16-05-26 Misattributed the track to Extreme Noise Terror (corrected). Also added some links thanks to Alexis Verger.

The post title comes from “Voice Your Opinion” by Unseen Terror. The version I have is from a Peel sessions compilation “Hardcore Holocaust”.

If I Can’t Change Your Mind

I have written previously about Journal Impact Factors (here and here). The response to these articles has been great and earlier this year I was asked to write something about JIFs and citation distributions for one of my favourite journals. I agreed and set to work.

Things started off so well. A title came straight to mind. In the style of quantixed, I thought The Number of The Beast would be amusing. I asked for opinions on Twitter and got an even better one (from Scott Silverman @sksilverman) Too Many Significant Figures, Not Enough Significance. Next, I found an absolute gem of a quote to kick off the piece. It was from the eminently quotable Sydney Brenner.

Before we develop a pseudoscience of citation analysis, we should remind ourselves that what matters absolutely is the scientific content of a paper and that nothing will substitute for either knowing it or reading it.

looseEndsThat quote was from a Loose Ends piece that Uncle Syd penned for Current Biology in 1995. Wow, 1995… that is quite a few years ago I thought to myself. Never mind. I pressed on.

There’s a lot of literature on JIFs, research assessment and in fact there are whole fields of scholarly activity (bibliometrics) devoted to this kind of analysis. I thought I’d better look back at what has been written previously. The “go to” paper for criticism of JIFs is Per Seglen’s analysis in the BMJ, published in 1997. I re-read this and I can recommend it if you haven’t already seen it. However, I started to feel uneasy. There was not much that I could add that hadn’t already been said, and what’s more it had been said 20 years ago.

Around about this time I was asked to review some fellowship applications for another EU country. The applicants had to list their publications, along with the JIF. I found this annoying. It was as if SF-DORA never happened.

There have been so many articles, blog posts and more written on JIFs. Why has nothing changed? It was then that I realised that it doesn’t matter how many things are written – however coherently argued – people like JIFs and they like to use them for research assessment. I was wasting my time writing something else. Sorry if this sounds pessimistic. I’m sure new trainees can be reached by new articles on this topic, but acceptance of JIF as a research assessment tool runs deep. It is like religious thought. No amount of atheist writing, no matter how forceful, cogent, whatever, will change people’s minds. That way of thinking is too deeply ingrained.

As the song says, “If I can’t change your mind, then no-one will”.

So I declared defeat and told the journal that I felt like I had said all that I could already say on my blog and that I was unable to write something for them. Apologies to all like minded individuals for not continuing to fight the good fight.

But allow me one parting shot. I had a discussion on Twitter with a few people, one of whom said they disliked the “JIF witch hunt”. This caused me to think about why the JIF has hung around for so long and why it continues to have support. It can’t be that so many people are statistically illiterate or that they are unscientific in choosing to ignore the evidence. What I think is going on is a misunderstanding. Criticism of a journal metric as being unsuitable to judge individual papers is perceived as an attack on journals with a high-JIF. Now, for good or bad, science is elitist and we are all striving to do the best science we can. Striving for the best for many scientists means aiming to publish in journals which happen to have a high JIF. So an attack of JIFs as a research assessment tool, feels like an attack on what scientists are trying to do every day.

JIFDistBecause of this intense focus on high-JIF journals… what people don’t appreciate is that the reality is much different. The distribution of JIFs is as skewed as that for the metric itself. What this means is that focussing on a minuscule fraction of papers appearing in high-JIF journals is missing the point. Most papers are in journals with low-JIFs. As I’ve written previously, papers in journals with a JIF of 4 get similar citations to those in a journal with a JIF of 6. So the JIF tells us nothing about citations to the majority of papers and it certainly can’t predict the impact of these papers, which are the majority of our scientific output.

So what about those fellowship applicants? All of them had papers in journals with low JIFs (<8). The applicants’ papers were indistinguishable in that respect. What advice would I give to people applying to such a scheme? Well, I wouldn’t advise not giving the information asked for. To be fair to the funding body they also asked for number of citations for each paper, but for papers that are only a few months old, this number is nearly always zero. My advice would be to try and make sure that your paper is available freely for anyone to read. Many of the applicants’ papers were outside my expertise and so the title and abstract didn’t tell me much about the significance of the paper. So I looked at some of these papers to look at the quality of the data in there… if I had access. Applicants who had published in closed access journals are at a disadvantage here because if I couldn’t download the paper then it was difficult to assess what they had been doing.

I was thinking that this post would be a meta-meta-blogpost. Writing about an article which was written about something I wrote on my blog. I suppose it still is, except the article was never finished. I might post again about JIFs, but for now I doubt I will have anything new to say that hasn’t already been said.

The post title is taken from “If I Can’t Change Your Mind” by Sugar from their LP Copper Blue. Bob Mould was once asked about song-writing and he said that the perfect song was like a maths puzzle (I can’t find a link to support this, so this is from memory). If you are familiar with this song, songwriting and/or mathematics, then you will understand what he means.

Edit @ 08:22 16-05-20 I found an interview with Bob Mould where he says song-writing is like city-planning. Maybe he just compares song-writing to lots of different things in interviews. Nonetheless I like the maths analogy.

Throes of Rejection: No link between rejection rates and impact?

I was interested in the analysis by Frontiers on the lack of a correlation between the rejection rate of a journal and the “impact” (as measured by the JIF). There’s a nice follow here at Science Open. The Times Higher Education Supplement also reported on this with the line that “mass rejection of research papers by selective journals in a bid to achieve a high impact factor is an enormous waste of academics’ time”.

First off, the JIF is a flawed metric in a number of ways but even at face value, what does this analysis really tell us?


This plot is taken from the post by Jon Tennant at Science Open.

As others have pointed out:

  1. The rejection rate is dominated by desk rejects, which although very annoying, don’t take that much time.
  2. Without knowing the journal name it is difficult to know what to make of the plot.

The data are available from Figshare and – thanks to Thomson-Reuters habit of reporting JIF to 3 d.p. – we can easily pull the journal titles from a list using JIF as a key. The list is here. Note that there may be errors due to this quick-and-dirty method.

The list takes on a different meaning when you can see the Journal titles alongside the numbers for rejection rate and JIF.



Looking for familiar journals – whichever field you are in – you will be disappointed. There’s an awful lot of noise in there. By this, I mean journals that are outside of your field.

This is the problem with this analysis as I see it. It is difficult to compare Nature Neuroscience with Mineralium Deposita…

My plan with this dataset was to replot rejection rate versus JIF2014 for a few different journal categories, but I don’t think there’s enough data to do this and make a convincing case one way or the other. So, I think the jury is still out on this question.

It would be interesting to do this analysis on a bigger dataset. Journals releasing their numbers on rejection rates would be a step forward to doing this.

One final note:

The Orthopedic Clinics of North America is a tough journal. Accepts only 2 papers in every 100 for an impact factor of 1!


The post title is from “Throes of Rejection” by Pantera from their Far Beyond Driven LP. I rejected the title “Satan Has Rejected my Soul” by Morrissey for obvious reasons.

The Great Curve II: Citation distributions and reverse engineering the JIF

There have been calls for journals to publish the distribution of citations to the papers they publish (1 2 3). The idea is to turn the focus away from just one number – the Journal Impact Factor (JIF) – and to look at all the data. Some journals have responded by publishing the data that underlie the JIF (EMBO J, Peer JRoyal Soc, Nature Chem). It would be great if more journals did this. Recently, Stuart Cantrill from Nature Chemistry actually went one step further and compared the distribution of cites at his journal with other chemistry journals. I really liked this post and it made me think that I should just go ahead and harvest the data for cell biology journals and post it.

This post is in two parts. First, I’ll show the data for 22 journals. They’re broadly cell biology, but there’s something for everyone with Cell, Nature and Science all included. Second, I’ll describe how I “reverse engineered” the JIF to get to these numbers. The second part is a bit technical but it describes how difficult it is to reproduce the JIF and highlights some major inconsistencies for some journals. Hopefully it will also be of interest to anyone wanting to do a similar analysis.

Citation distributions for 22 cell biology journals

The JIF for 2014 (published in the summer of 2015) is worked out by counting the total number of 2014 cites to articles in that journal that were published in 2012 and 2013. This number is divided by the number of “citable items” in that journal in 2012 and 2013. There are other ways to look at citation data, different windows to analyse, but this method is used here because it underlies the impact factor. I plotted out histograms to show the citation distributions at these journals from 0-50 citations, inset shows the frequency of papers with 50-1000 cites.



As you can see, the distributions are highly skewed and so reporting the mean is very misleading. Typically ~70% papers pick up less than the mean number of citations. Reporting the median is safer and is shown below. It shows how similar most of the journals are in this field in terms of citations to the average paper in that journal. Another metric, which I like, is the H-index for journals. Google Scholar uses this as a journal metric (using citation data from a 5-year window). For a journal, this is a number, h, which reveals how many papers got >=h citations. A plot of h-indices for these journals is shown below.


Here’s a summary table of all of this information together with the “official JIF” data, which is discussed below.

Journal Median H Citations Items Mean JIF Cites JIF Items JIF
Autophagy 3 18 2996 539 5.6 2903 247 11.753
Cancer Cell 14 37 5241 274 19.1 5222 222 23.523
Cell 19 72 28147 1012 27.8 27309 847 32.242
Cell Rep 6 26 6141 743 8.3 5993 717 8.358
Cell Res 3 19 1854 287 6.5 2222 179 12.413
Cell Stem Cell 14 37 5192 302 17.2 5233 235 22.268
Cell Mol Life Sci 4 19 3364 596 5.6 3427 590 5.808
Curr Biol 4 24 6751 1106 6.1 7293 762 9.571
Development 5 25 6069 930 6.5 5861 907 6.462
Dev Cell 7 23 3986 438 9.1 3922 404 9.708
eLife 5 20 2271 306 7.4 2378 255 9.3212
EMBO J 8 27 5828 557 10.5 5822 558 10.434
J Cell Biol 6 25 5586 720 7.8 5438 553 9.834
J Cell Sci 3 23 5995 1157 5.2 5894 1085 5.432
Mol Biol Cell 3 16 3415 796 4.3 3354 751 4.466
Mol Cell 11 37 8669 629 13.8 8481 605 14.018
Nature 12 105 69885 2758 25.3 71677 1729 41.296
Nat Cell Biol 13 35 5381 340 15.8 5333 271 19.679
Nat Rev Mol Biol Cell 8.5 43 5037 218 23.1 4877 129 37.806
Oncogene 5 26 6973 1038 6.7 8654 1023 8.459
Science 14 83 54603 2430 22.5 56231 1673 33.611
Traffic 3 11 1020 252 4.0 1018 234 4.350


Reverse engineering the JIF

The analysis shown above was straightforward. However, getting the data to match Thomson-Reuters’ calculations for the JIF was far from easy.

I downloaded the citation data from Web of Science for the 22 journals. I limited the search to “articles” and “reviews”, published in 2012 and 2013. I took the citation data from papers published in 2014 with the aim of plotting out the distributions. As a first step I calculated the mean citation for each journal (a.k.a. impact factor) to see how it compared with the official Journal Impact Factor (JIF). As you can see below, some were correct and others were off by some margin.

Journal Calculated IF JIF
Autophagy 5.4 11.753
Cancer Cell 14.8 23.523
Cell 23.9 32.242
Cell Rep 8.2 8.358
Cell Res 5.7 12.413
Cell Stem Cell 13.4 22.268
Cell Mol Life Sci 5.6 5.808
Curr Biol 5.0 9.571
Development 6.5 6.462
Dev Cell 7.5 9.708
eLife 6.0 9.322
EMBO J 10.5 10.434
J Cell Biol 7.6 9.834
J Cell Sci 5.2 5.432
Mol Biol Cell 4.1 4.466
Mol Cell 11.8 14.018
Nature 25.1 41.296
Nat Cell Biol 15.1 19.679
Nat Rev Mol Cell Biol 15.3 37.806
Oncogene 6.7 8.459
Science 18.6 33.611
Traffic 4.0 4.35

For most journals there was a large difference between this number and the official JIF (see below, left). This was not a huge surprise, I’d found previously that the JIF was very hard to reproduce (see also here). To try and understand the difference, I looked at the total citations in my dataset vs those from the official JIF. As you can see from the plot (right), my numbers are pretty much in agreement with those used for the JIF calculation. Which meant that the difference comes from the denominator – the number of citable items.


What the plots show is that, for most journals in my dataset, there are fewer papers considered as citable items by Thomson-Reuters. This is strange. I had filtered the data to leave only journal articles and reviews (which are citable items), so non-citable items should have been removed.

It’s no secret that the papers cited in the sum on the top of the impact factor calculation are not necessarily the same as the papers counted on the bottom.

Now, it’s no secret that the papers cited in the sum on the top of the impact factor calculation are not necessarily the same as the papers counted on the bottom (see here, here and here). This inconsistency actually makes plotting a distribution impossible. However, I thought that using the same dataset, filtering and getting to the correct total citation number meant that I had the correct list of citable items. So, what could explain this difference?

missingPapersI looked first at how big the difference in number of citable items is. Journals like Nature and Science are missing >1000 items(!), others are less and some such as Traffic, EMBO J, Development etc. have the correct number. Remember that journals carry different amounts of papers. So as a proportion of total papers the biggest fraction of missing papers was actually from Autophagy and Cell Research which were missing ~50% of papers classified in WoS as “articles” or “reviews”!

My best guess at this stage was that items were incorrectly tagged in Web of Science. Journals like Nature, Science and Current Biology carry a lot of obituaries, letters and other stuff that can fairly be removed from the citable items count. But these should be classified as such in Web of Science and therefore filtered out in my original search. Also, these types of paper don’t explain the big disparity in journals like Autophagy that only carry papers, reviews with a tiny bit of front matter.

PubmedCompI figured a good way forward would be to verify the numbers with another database – PubMed. Details of how I did this are at the foot of this post. This brought me much closer to the JIF “citable items” number for most journals. However, Autophagy, Current Biology and Science are still missing large numbers of papers. As a proportion of the size of the journal, Autophagy, Cell Research and Current Biology are missing the most. While Nature Cell Biology and Nature Reviews Molecular Cell Biology now have more citable items in the JIF calculation than are found in PubMed!

This collection of data was used for the citation distributions shown above, but it highlights some major discrepancies at least for some journals.

How does Thomson Reuters decide what is a citable item?

Some of the reasons for deciding what is a citable item are outlined in this paper. Of the six reasons that are revealed, all seem reasonable, but they suggest that they do not simply look at the classification of papers in the Web of Science database. Without wanting to pick on Autophagy – it’s simply the first one alphabetically – I looked at which was right: the PubMed number of 539 or the JIF number of 247 citable items published in 2012 and 2013. For the JIF number to be correct this journal must only publish ~10 papers per issue, which doesn’t seem to be right at least from a quick glance at the first few issues in 2012.

Why Thomson-Reuters removes some of these papers as non-citable items is a mystery… you can see from the histogram above that for Autophagy only 90 or so papers are uncited in 2014, so clearly the removed items are capable of picking up citations. If anyone has any ideas why the items were removed, please leave a comment.


Trying to understand what data goes into the Journal Impact Factor calculation (for some, but not all journals) is very difficult. This makes JIFs very hard to reproduce. As a general rule in science, we don’t trust things that can’t be reproduced, so why has the JIF persisted. I think most people realise by now that using this single number to draw conclusions about the excellence (or not) of a paper because it was published in a certain journal, is madness. Looking at the citation distributions, it’s clear that the majority of papers could be reshuffled between any of these journals and nobody would notice (see here for further analysis). We would all do better to read the paper and not worry about where it was published.

The post title is taken from “The Great Curve” by Talking Heads from their classic LP Remain in Light.

In PubMed, a research paper will have the publication type “journal article”, however other items can still have this publication type. These items also have additional types which can therefore be filtered. I retrieved all PubMed records from the journals published in 2012 and 2013 with publication type = “journal article”. This worked for 21 journals, eLife is online only so the ppdat field code had to be changed to pdat.

("Autophagy"[ta] OR "Cancer Cell"[ta] OR "Cell"[ta] OR "Cell Mol Life Sci"[ta] OR "Cell Rep"[ta] OR "Cell Res"[ta] OR "Cell Stem Cell"[ta] OR "Curr Biol"[ta] OR "Dev Cell"[ta] OR "Development"[ta] OR "Elife"[ta] OR "Embo J"[ta] OR "J Cell Biol"[ta] OR "J Cell Sci"[ta] OR "Mol Biol Cell"[ta] OR "Mol Cell"[ta] OR "Nat Cell Biol"[ta] OR "Nat Rev Mol Cell Biol"[ta] OR "Nature"[ta] OR "Oncogene"[ta] OR "Science"[ta] OR "Traffic"[ta]) AND (("2012/01/01"[PPDat] : "2013/12/31"[PPDat])) AND journal article[pt:noexp]

I saved this as an XML file and then pulled the values from the “publication type” key using Nokogiri/ruby (script). I then had a list of all the publication type combinations for each record. As a first step I simply counted the number of journal articles for each journal and then subtracted anything that was tagged as “biography”, “comment”, “portraits” etc. This could be done in IgorPro by making a wave indicating whether an item should be excluded (0 or 1) using the DOI as a lookup. This wave could then be used exclude papers from the distribution.

For calculation of the number of missing papers as a proportion of size of journal, I used the number of items from WoS for the WoS calculation, and the JIF number for the PubMed comparison.

Related to this, this IgorPro procedure will read in csv files from WoS/WoK. As mentioned in the main text, data were downloaded 500 records at a time as csv from WoS, using journal titles as a search term and limiting to “article” or “review” and limiting to 2012 and 2013. Note that limiting the search at the outset by year, limits the citation data you get back. You need to search first to get citations from all years and then refine afterwards. The files can be stitched together with the cat command.

cat *.txt > merge.txt

Edit 8/1/16 @ 07:41 Jon Lane told me via Twitter that Autophagy publishes short commentaries of papers in other journals called “Autophagic puncta” (you need to be a cell biologist to get this gag). He suggests these could be removed by Thomson Reuters for their calculation. This might explain the discrepancy for this journal. However, these items 1) cite other papers (so they contribute to JIF calculations), 2) they get cited (Jon says his own piece has been cited 18 times) so they are not non-citable items, 3) they’re tagged as though they are a paper or a review in WoS and PubMed.

What Difference Does It Make?

A few days ago, Retraction Watch published the top ten most-cited retracted papers. I saw this post with a bar chart to visualise these citations. It didn’t quite capture what the effect (if any) a retraction has on citations. I thought I’d quickly plot this out for the number one article on the list.


The plot is pretty depressing. The retraction has no effect on citations. Note that the retraction notice has racked up 125 citations, which could mean that at least some of the ~1000 citations to the original article that came after the retraction, acknowledge the fact that the article has been pulled.

The post title is taken from “What Difference Does it Make?” by The Smiths from ‘The Smiths’ and ‘Hatful of Hollow’

White label: the growth of bioRxiv

bioRxiv, the preprint server for biology, recently turned 2 years old. This seems a good point to take a look at how bioRxiv has developed over this time and to discuss any concerns sceptical people may have about using the service.

Firstly, thanks to Richard Sever (@cshperspectives) for posting the data below. The first plot shows the number of new preprints deposited and the number that were revised, per month since bioRxiv opened in Nov 2013. There are now about 200 preprints being deposited per month and this number will continue to increase. The cumulative article count (of new preprints) shows that, as of the end of last month, there are >2500 preprints deposited at bioRxiv. overall2


What is take up like across biology? To look at this, the number of articles in different subject categories can be totted up. Evolutionary Biology, Bioinformatics and Genomics/Genetics are the front-running disciplines. Obviously counting articles should be corrected for the size of these fields, but it’s clear that some large disciplines have not adopted preprinting in the same way. Cell biology, my own field, has some catching up to do. It’s likely that this reflects cultures within different fields. For example, genomics has a rich history of data deposition, sharing and openness. Other fields, less so…

So what are we waiting for?

I’d recommend that people wondering about preprinting go and read Stephen Curry’s post “just do it“. Any people who remain sceptical should keep reading…

Do I really want to deposit my best work on bioRxiv?

I’ve picked six preprints that were deposited in 2015. This selection demonstrates how important work is appearing first at bioRxiv and is being downloaded thousands of times before the papers appear in the pages of scientific journals.

  1. Accelerating scientific publishing in biology. A preprint about preprinting from Ron Vale, subsequently published in PNAS.
  2. Analysis of protein-coding genetic variation in 60,706 humans. A preprint summarising a huge effort from ExAC Exome Aggregation Consortium. 12,366 views, 4,534 downloads.
  3. TP53 copy number expansion correlates with the evolution of increased body size and an enhanced DNA damage response in elephants. This preprint was all over the news, e.g. Science.
  4. Sampling the conformational space of the catalytic subunit of human γ-secretase. CryoEM is the hottest technique in biology right now. Sjors Scheres’ group have been at the forefront of this revolution. This paper is now out in eLife.
  5. The genome of the tardigrade Hypsibius dujardini. The recent controversy over horizontal gene transfer in Tardigrades was rapidfire thanks to preprinting.
  6. CRISPR with independent transgenes is a safe and robust alternative to autonomous gene drives in basic research. This preprint concerning biosafety of CRISPR/Cas technology could be accessed immediately thanks to preprinting.

But many journals consider preprints to be previous publications!

Wrong. It is true that some journals have yet to change their policy, but the majority – including Nature, Cell and Science – are happy to consider manuscripts that have been preprinted. There are many examples of biology preprints that went on to be published in Nature (ancient genomes) and Science (hotspots in birds). If you are worried about whether the journal you want to submit your work to will allow preprinting, check this page first or the SHERPA/RoMEO resource. The journal “information to authors” page should have a statement about this, but you can always ask the Editor.

I’m going to get scooped

Preprints establish priority. It isn’t possible to be scooped if you deposit a preprint that is time-stamped showing that you were the first. The alternative is to send it to a journal where no record will exist that you submitted it if the paper is rejected, or sometimes even if they end up publishing it (see discussion here). Personally, I feel that the fear of scooping in science is overblown. In fields that are so hot that papers are coming out really fast the fear of scooping is high, everyone sees the work if its on bioRxiv or elsewhere – who was first is clear to all. Think of it this way: depositing a preprint at bioRxiv is just the same as giving a talk at a meeting. Preprints mean that there is a verifiable record available to everyone.

Preprints look ugly, I don’t want people to see my paper like that.

The depositor can format their preprint however they like! Check out Christophe Leterrier’s beautifully formatted preprint, or this one from Dennis Eckmeier. Both authors made their templates available so you can follow their example (1 and 2).

Yes but does -insert name of famous scientist- deposit preprints?

Lots of high profile scientists have already used bioRxiv. David Bartel, Ewan Birney, George Church, Ray Deshaies, Jennifer Doudna, Steve Henikoff, Rudy Jaenisch, Sophien Kamoun, Eric Karsenti, Maria Leptin, Rong Li, Andrew Murray, Pam Silver, Bruce Stillman, Leslie Vosshall and many more. Some sceptical people may find this argument compelling.

I know how publishing works now and I don’t want to disrupt the status quo

It’s paradoxical how science is all about pushing the frontiers, yet when it comes to publishing, scientists are incredibly conservative. Physics and Mathematics have been using preprinting as part of the standard route to publication for decades and so adoption by biology is nothing unusual and actually, we will simply be catching up. One vision for the future of scientific publishing is that we will deposit preprints and then journals will search out the best work from the server to highlight in their pages. The journals that will do this are called “overlay journals”. Sounds crazy? It’s already happening in Mathematics. Terry Tao, a Fields medal-winning mathematician recently deposited a solution to the Erdos discrepency problem on arXiv (he actually put them on his blog first). This was then “published” in Discrete Analysis, an overlay journal. Read about this here.

Disclaimer: other preprint services are available. F1000 Research, PeerJ Preprints and of course arXiv itself has quantitative biology section. My lab have deposited work at bioRxiv (1, 2 and 3) and I am an affiliate for the service, which means I check preprints before they go online.

Edit 14/12/15 07:13 put the scientists in alphabetical order. Added a part about scooping.

The post title comes from the term “white label” which is used for promotional vinyl copies of records ahead of their official release.