Sure To Fall

What does the life cycle of a scientific paper look like?

It stands to reason that after a paper is published, people download and read the paper and then if it generates sufficient interest, it will begin to be cited. At some point these citations will peak and the interest will die away as the work gets superseded or the field moves on. So each paper has a useful lifespan. When does the average paper start to accumulate citations, when do they peak and when do they die away?

Citation behaviours are known to be very field-specific. So to narrow things down, I focussed on cell biology and in one area “clathrin-mediated endocytosis” in particular. It’s an area that I’ve published in – of course this stuff is driven by self-interest. I downloaded data for 1000 papers from Web of Science that had accumulated the most citations. Reviews were excluded, as I assume their citation patterns are different from primary literature. The idea was just to take a large sample of papers on a topic. The data are pretty good, but there are some errors (see below).

Number-crunching (feel free to skip this bit): I imported the data into IgorPro making a 1D wave for each record (paper). I deleted the last point corresponding to cites in 2014 (the year is not complete). I aligned all records so that year of publication was 0. Next, the citations were normalised to the maximum number achieved in the peak year. This allows us to look at the lifecycle in a sensible way. Next I took out records to papers less than 6 years old as I reasoned these would have not have completed their lifecycle and could contaminate the analysis (it turned out to make little difference). The lifecycles were plotted and averaged. I also wrote a quick function to pull out the peak year for citations post hoc.

So what did it show?

Citations to a paper go up and go down, as expected (top left). When cumulative citations are plotted most of the articles have an initial burst and then level off. The exception are ~8 articles that continue to rise linearly (top right). On average a paper generates its peak citations three years after publication (box plot). The fall after this peak period is pretty linear and it’s apparently all over somewhere >15 years after publication (bottom left). To look at the decline in more detail I aligned the papers so that year 0 was the year of peak citations. The average now loses almost 40% of those peak citations in the following year and then declines steadily (bottom right).

Edit: The dreaded Impact Factor calculation takes the citations to articles published in the preceding 2 years and divides by the number of citable items in that period. This means that each paper only contributes to the Impact Factor in years 1 and 2. This is before the average paper reaches its peak citation period. Thanks to David Stephens (@david_s_bristol) for pointing this out. The alternative 5 year Impact Factor gets around this limitation.

Perhaps lifecycle is the wrong term: papers in this dataset don’t actually ‘die’, i.e. go to 0 citations. There is always a chance that a paper will pick up the odd citation. Papers published 15 years ago are still clocking 20% of their peak citations. Looking at papers cited at lower rates would be informative here.

Two other weaknesses that affect precision is that 1) a year is a long time and 2) publication is subject to long lag times. The analysis would be improved by categorising the records based on the month-year when the paper was published and the month-year when each citation comes in. Papers published in January in one year probably have a different peak than those published in December of the same year, but this is lost when looking at year alone. Secondly, due to publication lag, it is impossible to know when the peak period of influence for a paper truly is.
MisCytesProblems in the dataset. Some reviews remained despite being supposedly excluded, i.e. they are not properly tagged in the database. Also, some records have citations from years before the article was published! The numbers of citations are small enough to not worry for this analysis, but it makes you wonder about how accurate the whole dataset is. I’ve written before about how complete citation data may or may not be. These sorts of things are a concern for all of us who are judged by these things for hiring and promotion decisions.

The post title is taken from ‘Sure To Fall’ by The Beatles, recorded during The Decca Sessions.

Tips from the Blog I

What is the best music to listen to while writing a manuscript or grant proposal? OK, I know that some people prefer silence and certainly most people hate radio chatter while trying to concentrate. However, if you like listening to music, setting an iPod on shuffle is no good since a track by Napalm Death can jump from the speakers and affect your concentration. Here is a strategy for a randomised music stream of the right mood and with no repetition, using iTunes.

For this you need:
A reasonably large and varied iTunes library that is properly tagged*.

1. Setup the first smart playlist to select all songs in your library that you like to listen to while writing. I do this by selecting genres that I find conducive to writing.
Conditions are:
-Match any of the following rules
-Genre contains jazz
-add as many genres as you like, e.g. shoegaze, space rock, dream pop etc.
-Don’t limit and do check live updating
I call this list Writing

2. Setup a second smart playlist that makes a randomised novel list from the first playlist
Conditions are:
-Match all of the following rules
-Playlist is Writing   //or whatever you called the 1st playlist
-Last played is not in the last 14 days    //this means once the track is played it disappears, i.e. refreshes constantly
-Limit to 50 items selected by random
-Check Live updating
I call this list Writing List

That’s it! Now play from Writing List while you write. The same strategy works for other moods, e.g. for making figures I like to listen to different music and so I have another pair for that.

After a while, the tracks that you’ve skipped (for whatever reason) clog up the playlist. Just select all and delete from the smart playlist, this refreshes the list and you can go again with a fresh set.

* If your library has only a few tracks, or has plenty of tracks but they are all of a similar genre, this tip is not for you.

Outer Limits

This post is about a paper that was recently published. It was the result of a nice collaboration between me and Francisco López-Murcia and Artur Llobet in Barcelona.

The paper in a nutshell
The availability of clathrin sets a limit for presynaptic function

Background
Clathrin is a three legged protein that forms a cage around membranes during endoctosis. One site of intense clathrin-mediated endocytosis (CME) is the presynaptic terminal. Here, synaptic vesicles need to be recaptured after fusion and CME is the main route of retrieval. Clathrin is highly abundant in all cells and it is generally thought of as limitless for the formation of multiple clathrin-coated structures. Is this really true? In a neuron where there is a lot of endocytic activity, maybe the limits are tested?
It is known that strong stimulation of neurons causes synaptic depression – a form of reversible synaptic plasticity where the neuron can only evoke a weak postsynaptic response afterwards. Is depression a vesicle supply problem?

What did we find?
We showed that clathrin availability drops during stimulation that evokes depression. The drop in availability is due to clathrin forming vesicles and moving away from the synapse. We mimicked this by RNAi, dropping the clathrin levels and looking at synaptic responses. We found that when the clathrin levels drop, synaptic responses become very small. We noticed that fewer vesicles are able to be formed and those that do form are smaller. Interestingly, the amount of neurotransmitter (acetylcholine) in the vesicles was much less than the volume of the vesicles as measured by electron microscopy. This suggests there is an additional sorting problem in cells with lower clathrin levels.

Killer experiment
A third reviewer was called in (due to a split decision between Reviewers 1 and 2). He/she asked a killer question: all of our data could be due to an off-target effect of RNAi, could we do a rescue experiment? We spent many weeks to get the rescue experiment to work, but a second viral infection was too much for the cells and engineering a virus to express clathrin was very difficult. The referee also said: if clathrin levels set a limit for synaptic function, why don’t you just express more clathrin? Well, we would if we could! But this gave us an idea… why don’t we just put clathrin in the pipette and let it diffuse out to the synapses and rescue the RNAi phenotype over time? We did it – and to our surprise – it worked! The neurons went from an inhibited state to wild-type function in about 20 min. We then realised we could use the same method on normal neurons to boost clathrin levels at the synapse and protect against synaptic depression. This also worked! These killer experiments were a great addition to the paper and are a good example of peer review improving the paper.

People
Fran and Artur did almost all the experimental work. I did a bit of molecular biology and clathrin purification. Artur and I wrote the paper and put the figures together – lots of skype and dropbox activity.
Artur is a physiologist and his lab like to tackle problems that are experimentally very challenging – work that my lab wouldn’t dare to do – he’s the perfect collaborator. I have known Artur for years. We were postdocs in the same lab at the LMB in the early 2000s. We tried a collaborative project to inhibit dynamin function in adrenal chromaffin cells at that time, but it didn’t work out. We have stayed in touch and this is our first paper together. The situation in Spain for scientific research is currently very bad and it deteriorated while the project was ongoing. This has been very sad to hear about, but fortunately we were able to finish this project and we hope to work together more in the future.

We were on the cover!
25.coverNow the scientific literature is online, this doesn’t mean so much anymore, but they picked our picture for the cover. It is a single cell microculture expressing GFP that was stained for synaptic markers and clathrin. I changed the channels around for artistic effect.

What else?
J Neurosci is slightly different to other journals that I’ve published in recently (my only other J Neurosci paper was published in 2002). For the following reasons:

  1. No supplementary information. The journal did away with this years ago to re-introduce some sanity in the peer review process. This didn’t affect our paper very much. We had a movie of clathrin movement that would have gone into the SI at another journal, but we simply removed it here.
  2. ORCIDs for authors are published with the paper. This gives the reader access to all your professional information and distinguishes authors with similar names. I think this is a good idea.
  3. Submission fee. All manuscripts are subject to a submission fee. I believe this is to defray the costs of editorial work. I think this makes sense, although I’m not sure how I would feel if our paper had been rejected.

Reference:

López-Murcia, F.J., Royle, S.J. & Llobet, A. (2014) Presynaptic clathrin levels are a limiting factor for synaptic transmission J. Neurosci., 34: 8618-8629. doi: 10.1523/JNEUROSCI.5081-13.2014

Pubmed | Paper

The post title is taken from “Outer Limits” a 7″ Single by Sleep ∞ Over released in 2010.

All This And More

I was looking at the latest issue of Cell and marvelling at how many authors there are on each paper. It’s no secret that the raison d’être of Cell is to publish the “last word” on a topic (although whether it fulfils that objective is debatable). Definitive work needs to be comprehensive. So it follows that this means lots of techniques and ergo lots of authors. This means it is even more impressive when a dual author paper turns up in the table of contents for Cell. Anyway, I got to thinking: has it always been the case that Cell papers have lots of authors and if not, when did that change?

I downloaded the data for all articles published by Cell (and for comparison, J Cell Biol) from Scopus. The records required a bit of cleaning. For example, SnapShot papers needed to be removed and also the odd obituary etc. had been misclassified as an article. These could be quickly removed. I then went back through and filtered out ‘articles’ that were less than three pages as I think it is not possible for a paper to be two pages or fewer in length. The data could be loaded into IgorPro and boxplots generated per year to show how author number varied over time. Reviews that are misclassified as Articles will still be in the dataset, but I figured these would be minimal.

Authors1First off: Yes, there are more authors on average for a Cell paper versus a J Cell Biol paper. What is interesting is that both journals had similar numbers of authors when Cell was born (1974) and they crept up together until the early 2000s, when the number of Cell authors kept increasing, or JCell Biol flattened off, whichever way you look at it.

I think the overall trend to more authors is because understanding biology has increasingly required multiple approaches and the bar for evidence seems to be getting higher over time. The initial creep to more authors (1974-2000) might be due to a cultural change where people (technicians/students/women) began to get proper credit for their contributions. However, this doesn’t explain the divergence between J Cell Biol and Cell in recent years. One possibility is Cell takes more non-cell biology papers and that these papers necessarily have more authors. For example, the polar bear genome was published in Cell (29 authors), and this sort of paper would not appear in J Cell Biol. Another possibility is that J Cell Biol has a shorter and stricter revision procedure, which means that multiple rounds of revision, collecting new techniques and new authors is more limited than it is at Cell. Any other ideas?

AuthorI also quickly checked whether more authors means more citations, but found no evidence for such a relationship. For papers published in the years 2000-2004, the median citation number for papers with 1-10 authors was pretty constant for J Cell Biol. For Cell, these data mere more noisy. Three-author papers tended to be cited a bit more than those with two authors, but then four author papers were also lower.

The number of authors on papers from our lab ranges from 2-9 and median is 3.5. This would put an average paper from our lab in the bottom quartile for JCB and in the lower 10% for Cell in 2013. Ironically, our 9 author paper (an outlier) was published in J Cell Biol. Maybe we need to get more authors on our papers before we can start troubling Cell with our manuscripts…


The Post title is taken from ‘All This and More’ by The Wedding Present from their LP George Best.

Blast Off!

This post is about metrics and specifically the H-index. It will probably be the first of several on this topic.

I was re-reading a blog post by Alex Bateman on his affection for the H-index as a tool for evaluating up-and-coming scientists. He describes Jorge Hirsch’s H-index, its limitations and its utility quite nicely, so I won’t reiterate this (although I’ll probably do so in another post). What is under-appreciated is that Hirsch also introduced the m quotient, which is the H-index divided by years since the first publication. It’s the m quotient that I’ll concentrate on here. The TL;DR is: I think that the H-index does have some uses, but evaluating early career scientists is not one of them.

Anyone of an anti-metrics disposition should look away now.

Alex proposes that the scientists can be judged (and hired) by using m as follows:

  • <1.0 = average scientist
  • 1.0-2.0 = above average
  • 2.0-3.0 = excellent
  • >3.0 = stellar

He says “So post-docs with an m-value of greater than three are future science superstars and highly likely to have a stratospheric rise. If you can find one, hire them immediately!”.

From what I have seen, the H-index (and therefore m) is too noisy for early stage career scientists to be of any use for evaluation. Let’s leave that aside for the moment. What he is saying is you should definitely hire a post-doc who has published ≥3 papers with ≥3 citations each in their first year, ≥6 with ≥6 citations each in their second year, ≥9 papers with ≥9 in their third year…

Do these people even exist? A candidate with 3 year PhD and a 3 year postdoc (6 would mean ≥18 papers with ≥18 citations each! In my field (molecular cell biology), it is unusual for somebody to publish that many papers, let alone accrue citations at that rate*.

This got me thinking: using Alex’s criteria, how many stellar scientists would we miss out on and would we be more likely to hire the next Jan Hendrik Schön. To check this out I needed to write a quick program to calculate H-index by year (I’ll describe this in a future post). Off the top of my head I thought of a few scientists that I know of, who are successful by many other measures, and plotted their H-index by year. The dotted line shows a constant m of 1,  “average” by Alex’s criteria. I’ve taken a guess at when they became a PI. I have anonymised the scholars, the information is public and anyone can calculate this, but it’s not fair to identify people without asking (hopefully they can’t recognise themselves – if they read this!).

This is a small sample taken from people in my field. You can see that it is rare for scientists to have a big m at an early stage in their careers. With the exception of Scholar C, who was just awesome from the get-go, panels appointing any of these scholars would have had trouble divining the future success of these people on the basis of H-index and m alone. Scholar D and Scholar E really saw their careers take-off by making big discoveries, and these happened at different stages of their careers. Both of these scholars were “below average” when they were appointed as PI. The panel would certainly not have used metrics in their evaluation (the databases were not in wide use back then), probably just letters of recommendation and reading the work. Clearly, they could identify the potential in these scientists… or maybe they just got lucky. Who knows?!

There may be other fields where publication at higher rates can lead to a large m but I would still question the contribution of the scientist to the papers that led to the H-index. Are they first or last author? One problem with the H-index is that the 20th scientist in a list of 40 authors gets the same credit as the first author. Filtering what counts in the list of articles seems sensible, but this would make the values even more noisy for early stage scientists.

 

*In the comments section, somebody points out that if you publish a paper very early then this affects your m value. This is something I sympathise with. My first paper was in 1999 when I was an undergrad. This dents my m value as it was a full three years until my next paper.

The post title is taken from ‘Blast Off!’ by Rivers Cuomo from ‘Songs from the Black Hole’ the unreleased follow-up to Pinkerton.

Falling and Landing

A great quote from a classic paper by J.B.S. Haldane “On Being The Right Size” (1926).

You can drop a mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it gets a slight shock and walks away, provided that the ground is fairly soft. A rat is killed, a man is broken, a horse splashes.

The paper is available here.


The post title is taken from ‘Falling and Landing’ by The Delgados from their LP ‘Domestiques’.

Into The Great Wide Open

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

I thought I would write a post on how this paper came to be and also about our first proper experience with preprinting.

Title of the paper: Non-specificity of Pitstop 2 in clathrin-mediated endocytosis.

In a nutshell: we show that Pitstop 2, a supposedly selective clathrin inhibitor acts in a non-specific way to inhibit endocytosis.

Authors: Anna Willox, who was a postdoc in the lab from 2008-2012, did the flow cytometry measurements and Yasmina Sahraoui who was a summer student in my lab, did the binding experiments. And me.

Background: The description of “pitstops” – small molecules that inhibit clathrin-mediated endocytosis – back in 2011 in Cell was heralded as a major step-forward in cell biology. And it really would be a breakthrough if we had ways to selectively switch off clathrin-mediated endocytosis. Lots of nasty things gain entry into cells by hijacking this pathway, including viruses such as HIV and so if we could stop viral entry this could prevent cellular infection. Plus, these reagents would be really handy in the lab for cell biologists.

The rationale for designing the pitstop inhibitors was that they should block the interaction between clathrin and adaptor proteins. Adaptors are the proteins that recognise the membrane and cargo to be internalised – clathrin itself cannot do this. So if we can stop clathrin from binding adaptors there should be no internalisation – job done! Now, in 2000 or so, we thought that clathrin binds to adaptors via a single site on its N-terminal domain. This information was used in the drug screen that identified pitstops. The problem is that, since 2000, we have found that there are four sites on the N-terminal domain of clathrin that can each mediate endocytosis. So blocking one of these sites with a drug, would do nothing. Despite this, pitstop compounds, which were shown to have a selectivity for one site on the N-terminal domain of clathrin, blocked endocytosis. People in the field scratched their hands at how this is possible.

A damning paper was published in 2012 from Julie Donaldson’s lab showing that pitstops inhibit clathrin-independent endocytosis as well as clathrin-mediated endocytosis. Apparently, the compounds affect the plasma membrane and so all internalisation is inhibited. Many people thought this was the last that we would hear about these compounds. After all, these drugs need to be highly selective to be any use in the lab let alone in the clinic.

Our work: we had our own negative results using these compounds, sitting on our server, unpublished. Back in February 2011, while the Pitstop paper was under revision, the authors of that study sent some of these compounds to us in the hope that we could use these compounds to study clathrin on the mitotic spindle. The drugs did not affect clathrin binding to the spindle (although they probably should have done) and this prompted us to check whether the compounds were working – they had been shipped all the way from Australia so maybe something had gone wrong. We tested for inhibition of clathrin-mediated endocytosis and they worked really well.

At the time we were testing the function of each of the four interaction sites on clathrin in endocytosis, so we added Pitstop 2 to our experiments to test for specificity. We found  that Pitstop 2 inhibits clathrin-mediated endocytosis even when the site where Pitstops are supposed to bind, has been mutated! The picture shows that the compound (pink) binds where sequences from adaptors can bind. Mutation of this site doesn’t affect endocytosis, because clathrin can use any three of the other four sites. Yet Pitstop blocks endocytosis mediated by this mutant, so it must act elsewhere, non-specifically.

So the compounds were not as specific as claimed, but what could we do with this information? There didn’t seem enough to publish and I didn’t want people in the lab working on this as it would take time and energy away from other projects. Especially when debunking other people’s work is such a thankless task (why this is the case, is for another post). The Dutta & Donaldson paper then came out, which was far more extensive than our results and so we moved on.

What changed?

A few things prompted me to write this work up. Not least, Yasmina had since shown that our mutations were sufficient to prevent AP-2 binding to clathrin. This result filled a hole in our work. These things were:

  1. People continuing to use pitstops in published work, without acknowledging that they may act non-specifically. The turning point was this paper, which was critical of the Dutta & Donaldson work.
  2. People outside of the field using these compounds without realising their drawbacks.
  3. AbCam selling this compound and the thought of other scientists buying it and using it on the basis of the original paper made me feel very guilty that we had not published our findings.
  4. It kept getting easier and easier to publish “negative results”. Journals such as Biology Open from Company of Biologists or PLoS ONE and preprint servers (see below) make this very easy.

Finally, it was a twitter conversation with Jim Woodgett convinced me that, when I had the time, I would write it up.

To which, he replied:

I added an acknowledgement to him in our paper! So that, together with the launch of bioRxiv, convinced me to get the paper online.

The Preprinting Experience

This paper was our first proper preprint. We had put an accepted version of our eLife paper on bioRxiv before it came out in print at eLife, but that doesn’t really count. For full disclosure, I am an affiliate of bioRxiv.

The preprint went up on 13th February and we submitted it straight to Biology Open the next day. I had to check with the Journal that it was OK to submit a deposited paper. At the time they didn’t have a preprint policy (although I knew that David Stephens had submitted his preprinted paper there and he told me their policy was about to change). Biology Open now accept preprinted papers – you can check which journals do and which ones don’t here.

My idea was that I just wanted to get the information into the public domain as fast as possible. The upshot was, I wasn’t so bothered about getting feedback on the manuscript. For those that don’t know: the idea is that you deposit your paper, get feedback, improve your paper then submit it for publication. In the end I did get some feedback via email (not on the bioRxiv comments section), and I was able to incorporate those changes into the revised version. I think next time, I’ll deposit the paper and wait one week while soliciting comments and then submit to a journal.

It was viewed quite a few times in the time while the paper was being considered by Biology Open. I spoke to a PI who told me that they had found the paper and stopped using pitstop as a result. I think this means getting the work out there was worth it after all.

Now it is out “properly” in Biology Open and anyone can read it.

Verdict: I was really impressed by Biology Open. The reviewing and editorial work were handled very fast. I guess it helps that the paper was very short, but it was very uncomplicated. I wanted to publish with Biology Open rather than PLoS ONE as the Company of Biologists support cell biology in the UK. Disclaimer: I am on the committee of the British Society of Cell Biology which receives funding from CoB.

Depositing the preprint at bioRxiv was easy and for this type of paper, it is a no-brainer. I’m still not sure to what extent we will preprint our work in the future. This is unchartered territory that is evolving all the time, we’ll see. I can say that the experience for this paper was 100% positive.

References

Dutta, D., Williamson, C. D., Cole, N. B. and Donaldson, J. G. (2012) Pitstop 2 is a potent inhibitor of clathrin-independent endocytosis. PLoS One 7, e45799.

Lemmon, S. K. and Traub, L. M. (2012) Getting in Touch with the Clathrin Terminal Domain. Traffic, 13, 511-9.

Stahlschmidt, W., Robertson, M. J., Robinson, P. J., McCluskey, A. and Haucke, V. (2014) Clathrin terminal domain-ligand interactions regulate sorting of mannose 6-phosphate receptors mediated by AP-1 and GGA adaptors. J Biol Chem. 289, 4906-18.

von Kleist, L., Stahlschmidt, W., Bulut, H., Gromova, K., Puchkov, D., Robertson, M. J., MacGregor, K. A., Tomilin, N., Pechstein, A., Chau, N. et al. (2011) Role of the clathrin terminal domain in regulating coated pit dynamics revealed by small molecule inhibition. Cell 146, 471-84.

Willox, A.K., Sahraoui, Y.M.E. & Royle, S.J. (2014) Non-specificity of Pitstop 2 in clathrin-mediated endocytosis Biol Open, doi: 10.1242/​bio.20147955.

Willox, A.K., Sahraoui, Y.M.E. & Royle, S.J. (2014) Non-specificity of Pitstop 2 in clathrin-mediated endocytosis bioRxiv, doi: 10.1101/002675.

The post title is taken from ‘Into The Great Wide Open’ by Tom Petty and The Heartbreakers from the LP of the same name.

Give, Give, Give Me More, More, More

A recent opinion piece published in eLife bemoaned the way that citations are used to judge academics because we are not even certain of the veracity of this information. The main complaint was that Google Scholar – a service that aggregates citations to articles using a computer program – may be less-than-reliable.

There are three main sources of citation statistics: Scopus, Web of Knowledge/Science and Google Scholar; although other sources are out there. These are commonly used and I checked out how comparable these databases are for articles from our lab.

The ratio of citations is approximately 1:1:1.2 for Scopus:WoK:GS. So Google Scholar is a bit like a footballer, it gives 120%.

I first did this comparison in 2012 and again in 2013. The ratio has remained constant, although these are the same articles, and it is a very limited dataset. In the eLife opinion piece, Eve Marder noted an extra ~30% citations for GS (although I calculated it as ~40%, 894/636=1.41). Talking to colleagues, they have also noticed this. It’s clear that there is some inflation with GS, although the degree of inflation may vary by field. So where do these extra citations come from?

  1. Future citations: GS is faster than Scopus and WoK. Articles appear there a few days after they are published, whereas it takes several weeks or months for the same articles to appear in Scopus and WoK.
  2. Other papers: some journals are not in Scopus and WoK. Again, these might be new journals that aren’t yet included at the others, but GS doesn’t discriminate and includes all papers it finds. One of our own papers (an invited review at a nascent OA journal) is not covered by Scopus and WoK*. GS picks up preprints whereas the others do not.
  3. Other stuff: GS picks up patents and PhD theses. While these are not traditional papers, published in traditional journals, they are clearly useful and should be aggregated.
  4. Garbage: GS does pick up some stuff that is not a real publication. One example is a product insert for an antibody, which has a reference section. Another is duplicate publications. It is quite good at spotting these and folding them into a single publication, but some slip through.

OK, Number 4 is worrying, but the other citations that GS detects versus Scopus and WoK are surely a good thing. I agree with the sentiment expressed in the eLife paper that we should be careful about what these numbers mean, but I don’t think we should just disregard citation statistics as suggested.

GS is free, while the others are subscription-based services. It did look for a while like Google was going to ditch Scholar, but a recent interview with the GS team (sorry, I can’t find the link) suggests that they are going to keep it active and possibly develop it further. Checking out your citations is not just an ego-trip, it’s a good way to find out about articles that are related to your own work. GS has a nice feature that send you an email whenever it detects a citation for your profile. The downside of GS is that its terms of service do not permit scraping and reuse, whereas downloading of subsets of the other databases is allowed.

In summary, I am a fan of Google Scholar. My page is here.

 

* = I looked into this a bit more and the paper is actually in WoK, it has no Title and it has 7 citations (versus 12 in GS). Although it doesn’t come up in a search for Fiona or for me.

hood

 

However, I know from GS that this paper was also cited in a paper by the Cancer Genome Atlas Network in Nature. WoK listed this paper as having 0 references and 0 citations(!). Does any of this matter? Well, yes. WoK is a Thomson Reuters product and is used as the basis for their dreaded Impact Factor – which (like it or not) is still widely used for decision making. Also many Universities use WoK information in their hiring and promotions processes.

The post title comes from ‘Give, Give, Give Me More, More, More’ by The Wonder Stuff from the LP ‘Eight Legged Groove Machine’. Finding a post title was difficult this time. I passed on: Pigs (Three Different Ones) and Juxtapozed with U. My iTunes library is lacking songs about citations…

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