Waiting to Happen: Publication lag times in Cell Biology Journals

My interest in publication lag times continues. Previous posts have looked at how long it takes my lab to publish our work, how often trainees publish and I also looked at very long lag times at Oncogene. I recently read a blog post on automated calculation of publication lag times for Bioinformatics journals. I thought it would be great to do this for Cell Biology journals too. Hopefully people will find it useful and can use this list when thinking about where to send their paper.

What is publication lag time?

If you are reading this, you probably know how science publication works. Feel free to skip. Otherwise, it goes something like this. After writing up your work for publication, you submit it to a journal. Assuming that this journal will eventually publish the paper (there is usually a period of submitting, getting rejected, resubmitting to a different journal etc.), they receive the paper on a certain date. They send it out to review, they collate the reviews and send back a decision, you (almost always) revise your paper further and then send it back. This can happen several times. At some point it gets accepted on a certain date. The journal then prepares the paper for publication in a scheduled issue on a specific date (they can also immediately post papers online without formatting). All of these steps add significant delays. It typically takes 9 months to publish a paper in the biomedical sciences. In 2015 this sounds very silly, when world-wide dissemination of information is as simple as a few clicks on a trackpad. The bigger problem is that we rely on papers as a currency to get jobs or funding and so these delays can be more than just a frustration, they can affect your ability to actually do more science.

The good news is that it is very straightforward to parse the received, accepted and published dates from PubMed. So we can easily calculate the publication lags for cell biology journals. If you don’t work in cell biology, just follow the instructions below to make your own list.

The bad news is that the deposition of the date information in PubMed depends on the journal. The extra bad news is that three of the major cell biology journals do not deposit their data: J Cell Biol, Mol Biol Cell and J Cell Sci. My original plan was to compare these three journals with Traffic, Nat Cell Biol and Dev Cell. Instead, I extended the list to include other journals which take non-cell biology papers (and deposit their data).

LagTimes1

A summary of the last ten years

Three sets of box plots here show the publication lags for eight journals that take cell biology papers. The journals are Cell, Cell Stem Cell, Current Biology, Developmental Cell, EMBO Journal, Nature Cell Biology, Nature Methods and Traffic (see note at the end about eLife). They are shown in alphabetical order. The box plots show the median and the IQR, whiskers show the 10th and 90th percentiles. The three plots show the time from Received-to-Published (Rec-Pub), and then a breakdown of this time into Received-to-Accepted (Rec-Acc) and Accepted-to-Published (Rec-Pub). The colours are just to make it easier to tell the journals apart and don’t have any significance.

You can see from these plots that the journals differ widely in the time it takes to publish a paper there. Current Biology is very fast, whereas Cell Stem Cell is relatively slow. The time it takes the journals to move them from acceptance to publication is pretty constant. Apart from Traffic where it takes an average of ~3 months to get something in to print. Remember that the paper is often online for this period so this is not necessarily a bad thing. I was not surprised that Current Biology was the fastest. At this journal, a presubmission inquiry is required and the referees are often lined up in advance. The staff are keen to publish rapidly, hence the name, Current Biology. I was amazed at Nature Cell Biology having such a short time from Received-to-Acceptance. The delay in Review-to-Acceptance comes from multiple rounds of revision and from doing extra experimental work. Anecdotally, it seems that the review at Nature Cell Biol should be just as lengthy as at Dev Cell or EMBO J. I wonder if the received date is accurate… it is possible to massage this date by first rejecting the paper, but allowing a resubmission. Then using the resubmission date as the received date [Edit: see below]. One way to legitimately limit this delay is to only allow a certain time for revisions and only allow one round of corrections. This is what happens at J Cell Biol, unfortunately we don’t have this data to see how effective this is.

lagtimes2

How has the lag time changed over the last ten years?

Have the slow journals always been slow? When did they become slow?  Again three plots are shown (side-by-side) depicting the Rec-Pub and then the Rec-Acc and Acc-Pub time. Now the intensity of red or blue shows the data for each year (2014 is the most intense colour). Again you can see that the dataset is not complete with missing date information for Traffic for many years, for example.

Interestingly, the publication lag has been pretty constant for some journals but not others. Cell Stem Cell and Dev Cell (but not the mothership – Cell) have seen increases as have Nature Cell Biology and Nature Methods. On the whole Acc-Pub times are stable, except for Nature Methods which is the only journal in the list to see an increase over the time period. This just leaves us with the task of drawing up a ranked list of the fastest to the slowest journal. Then we can see which of these journals is likely to delay dissemination of our work the most.

The Median times (in days) for 2013 are below. The journals are ranked in order of fastest to slowest for Received-to-Publication. I had to use 2013 because EMBO J is missing data for 2014.

Journal Rec-Pub Rec-Acc Acc-Pub
Curr Biol 159 99.5 56
Nat Methods 192 125 68
Cell 195 169 35
EMBO J 203 142 61
Nature Cell Biol 237 180 59
Traffic 244 161 86
Dev Cell 247 204 43
Cell Stem Cell 284 205 66

You’ll see that only Cell Stem Cell is over the threshold where it would be faster to conceive and give birth to a human being than to publish a paper there (on average). If the additional time wasted in submitting your manuscript to other journals is factored in, it is likely that most papers are at least on a par with the median gestation time.

If you are wondering why eLife is missing… as a new journal it didn’t have ten years worth of data to analyse. It did have a reasonably complete set for 2013 (but Rec-Acc only). The median time was 89 days, beating Current Biology by 10.5 days.

Methods

Please check out Neil Saunders’ post on how to do this. I did a PubMed search for (journal1[ta] OR journal2[ta] OR ...) AND journal article[pt] to make sure I didn’t get any reviews or letters etc. I limited the search from 2003 onwards to make sure I had 10 years of data for the journals that deposited it. I downloaded the file as xml and I used Ruby/Nokogiri to parse the file to csv. Installing Nokogiri is reasonably straightforward, but the documentation is pretty impenetrable. The ruby script I used was from Neil’s post (step 3) with a few lines added:


#!/usr/bin/ruby

require 'nokogiri'

f = File.open(ARGV.first)
doc = Nokogiri::XML(f)
f.close

doc.xpath("//PubmedArticle").each do |a|
r = ["", "", "", "", "", "", "", "", "", "", ""]
r[0] = a.xpath("MedlineCitation/Article/Journal/ISOAbbreviation").text
r[1] = a.xpath("MedlineCitation/PMID").text
r[2] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Year").text
r[3] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Month").text
r[4] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='received']/Day").text
r[5] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Year").text
r[6] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Month").text
r[7] = a.xpath("PubmedData/History/PubMedPubDate[@PubStatus='accepted']/Day").text
r[8] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Year").text
r[9] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Month").text
r[10] = a.xpath("MedlineCitation/Article/Journal/JournalIssue/Pubdate/Day").text
puts r.join(",")
end

and then executed as described. The csv could then be imported into IgorPro and processed. Neil’s post describes a workflow for R, or you could use Excel or whatever at this point. As he notes, quite a few records are missing the date information and some of it is wrong, i.e. published before it was accepted. These need to be cleaned up. The other problem is that the month is sometimes an integer and sometimes a three-letter code. He uses lubridate in R to get around this, a loop-replace in Igor is easy to construct and even Excel can handle this with an IF statement, e.g. IF(LEN(G2)=3,MONTH(1&LEFT(G2,3)),G2) if the month is in G2. Good luck!

Edit 9/3/15 @ 17:17 several people (including Deborah Sweet and Bernd Pulverer from Cell Press/Cell Stem Cell and EMBO, respectively) have confirmed via Twitter that some journals use the date of resubmission as the submitted date. Cell Stem Cell and EMBO journals use the real dates. There is no way to tell whether a journal does this or not (from the deposited data). Stuart Cantrill from Nature Chemistry pointed out that his journal do declare that they sometimes reset the clock. I’m not sure about other journals. My own feeling is that – for full transparency – journals should 1) record the actual dates of submission, acceptance and publication, 2) deposit them in PubMed and add them to the paper. As pointed out by Jim Woodgett, scientists want the actual dates on their paper, partly because they are the real dates, but also to claim priority in certain cases. There is a conflict here, because journals might appear inefficient if they have long publication lag times. I think this should be an incentive for Editors to simplify revisions by giving clear guidance and limiting successive revision cycles. (This Edit was corrected 10/3/15 @ 11:04).

The post title is taken from “Waiting to Happen” by Super Furry Animals from the “Something 4 The Weekend” single.

Half Right

I was talking to a speaker visiting our department recently. While discussing his postdoc work from years ago, he told me about the identification of the sperm factor that causes calcium oscillations in the egg at fertilisation. It was an interesting tale because the group who eventually identified the factor – now widely accepted as PLCzeta – had earlier misidentified the factor, naming it oscillin.

The oscillin paper was published in Nature in 1996 and the subsequent (correct) paper was published in Development in 2002. I wondered what the citation profiles of these papers looks like now.

plcz

As you can see there was intense interest in the first paper that quickly petered out, presumably when people found out that oscillin was a contaminant and not the real factor. The second paper on the other hand has attracted a large number of citations and continues to do so 12 years later – a sign of a classic paper. However, the initial spike in citations was not as high as the Nature paper.

The impact factor of Nature is much higher than that of Development. I’ve often wondered if this is due to a sociological phenomenon: people like to cite Cell/Nature/Science papers rather than those at other journals and this bumps up the impact factor. Before you comment, yes I know there are other reasons, but the IFs do not change much over time and I wonder whether journal hierarchy explains the hardiness of IFs over time. Anyway, these papers struck me as a good test of the idea… Here we have essentially the same discovery, reported by the same authors. The only difference here is the journal (and that one paper is six years after the other). Normally it is not possible to test if the journal influences citations because a paper cannot erased and republished somewhere else. The plot suggests that Nature papers inherently attract much more cites than those in Development, presumably because of the exposure of publishing there. From the graph, it’s not difficult to see that even if a paper turns out not to be right, it can still boost the IF of the journal during the window of assessment. Another reason not to trust journal impact factors.

I can’t think of any way to look at this more systematically to see if this phenomenon holds true. I just thought it was interesting, so I’ll leave it here.


The post title is taken from Half Right by Elliott Smith from the posthumous album New Moon. Bootlegs have the title as Not Half Right, which would also be appropriate.

What The World Is Waiting For

The transition for scientific journals from print to online has been slow and painful. And it is not yet complete. This week I got an RSS alert to a “new” paper in Oncogene. When I downloaded it, something was familiar… very familiar… I’d read it almost a year ago! Sure enough, the AOP (ahead of print or advance online publication) date for this paper was September 2013 and here it was in the August 2014 issue being “published”.

I wondered why a journal would do this. It is possible that delaying actual publication would artificially boost the Impact Factor of a journal because there is a delay before citations roll in and citations also peak after two years. So if a journal delays actual publication, then the Impact Factor assessment window captures a “hotter” period when papers are more likely to generate more citations*. Richard Sever (@cshperspectives) jumped in to point out a less nefarious explanation – the journal obviously has a backlog of papers but is not allowed to just print more papers to catch up, due to page budgets.

There followed a long discussion about this… which you’re welcome to read. I was away giving a talk and missed all the fun, but if I may summarise on behalf of everybody: isn’t it silly that we still have pages – actual pages, made of paper – and this is restricting publication.

I wondered how Oncogene got to this position. I retrieved the data for AOP and actual publication for the last five years of papers at Oncogene excluding reviews, from Pubmed. Using oncogene[ta] NOT review[pt] as a search term. The field DP has the date published (the “issue date” that the paper appears in print) and PHST has several interesting dates including [aheadofprint]. These could be parsed and imported into IgorPro as 1D waves. The lag time from AOP to print could then be calculated. I got 2916 papers from the search and was able to get data for 2441 papers.

OncogeneLagTimeYou can see for this journal that the lag time has been stable at around 300 days (~10 months) for issues published since 2013. So a paper AOP in Feb 2012 had to wait over 10 months to make it into print. This followed a linear period of lag time growth from mid-2010.

I have no links to Oncogene and don’t particularly want to single them out. I’m sure similar lags are happening at other print journals. Actually, my only interaction with Oncogene was that they sent this paper of ours out to review in 2011 (it got two not-negative-but-admittedly-not-glowing reviews) and then they rejected it because they didn’t like the cell line we used. I always thought this was a bizarre decision: why couldn’t they just decide that before sending it to review and wasting our time? Now, I wonder whether they were not keen to add to their increasing backlog of papers at their journal? Whatever the reason, it has put me off submitting other papers there.

I know that there are good arguments for continuing print versions of journals, but from a scientist’s perspective the first publication is publication. Any subsequent versions are simply redundant and confusing.

*Edit: Alexis Verger (@Alexis_Verger) pointed me to a paper which describes that, for neuroscience journals, the lag time has increased over time. Moreover, the authors suggest that this is for the purpose of maximising Journal Impact Factor.

The post title comes from the double A-side Fools Gold/What The World Is Waiting For by The Stone Roses.

Six Plus One

Last week, ALM (article-level metric) data for PLoS journals were uploaded to Figshare with the invitation to do something cool with it.

Well, it would be rude not to. Actually, I’m one of the few scientists on the planet that hasn’t published a paper with Public Library of Science (PLoS), so I have no personal agenda here. However, I love what PLoS is doing and what it has achieved to disrupt the scientific publishing system. Anyway, what follows is not in any way comprehensive, but I was interested to look at a few specific things:

  1. Is there a relationship between Twitter mentions and views of papers?
  2. What is the fraction of views that are PDF vs HTML?
  3. Can citations be predicted by more immediate article level metrics?

The tl;dr version is 1. Yes. 2. ~20%. 3. Can’t say but looks unlikely.

1. Twitter mentions versus paper views

All PLoS journals are covered. The field containing paper views is (I think) “Counter” this combines views of HTML and PDF (see #2). A plot of Counter against Publication Date for all PLoS papers (upper plot) shows that the number of papers published has increased dramatically since the introduction of PLoS ONE in 2007. There is a large variance in number of views, which you’d expect and also, the views tail off for the most recent papers, since they have had less time to accumulate views. Below is the same plot where the size and colour of the markers reflects their Twitter score (see key). There’s a sharp line that must correspond to the date when Twitter data was logged as an ALM. There’s a scattering of mentions after this date to older literature, but one 2005 paper stands out – Ioannidis’s paper Why Most Published Research Findings Are False. It has a huge number of views and a large twitter score, especially considering that it was a seven year old paper when they started recording the data. A pattern emerges in the post-logging period. Papers with more views are mentioned more on Twitter. The larger darker markers are higher on the y-axis. Mentioning a paper on Twitter is sure to generate views of the paper, at some (unknown) conversion rate. However, as this is a single snapshot, we don’t know if Twitter mentions drive more downloads of papers, or whether more “interesting”/highly downloaded work is talked about more on Twitter.

twitter_counter

2. Fraction of PDF vs HTML views

I asked a few people what they thought the download ratio is for papers. Most thought 60-75% as PDF versus 40-25% HTML. I thought it would be lower, but I was surprised to see that it is, at most, 20% for PDF. The plot below shows the fraction of PDF downloads (counter_pdf/(counter_pdf+counter_html)). For all PLoS journals, and then broken down for PLoS Biol, PLoS ONE.

PDF-FractionThis was a surprise to me. I have colleagues who don’t like depositing post-print or pre-print papers because they say that they prefer their work to be seen typeset in PDF format. However, this shows that, at least for PLoS journals, the reader is choosing to not see a typeset PDF at all, but a HTML version.

Maybe the PLoS PDFs are terribly formatted and 80% people don’t like them. There is an interesting comparison that can be done here, because all papers are deposited at Pubmed Central (PMC) and so the same plot can be generated for the PDF fraction there. The PDF format is different to PLoS and so we can test the idea that people prefer HTML over PDF at PLoS because they don’t like the PLoS format.

PMCGraph

The fraction of PDF downloads is higher, but only around 30%. So either the PMC format is just as bad, or this is the way that readers like to consume the scientific literature. A colleague mentioned that HTML views are preferable to PDF if you want to actually want to do something with the data, e.g. for meta-analysis. This could have an effect. HTML views could be skim reading, whereas PDF is for people who want to read in detail… I wonder whether these fractions are similar at other publishers, particularly closed access publishers?

3. Citation prediction?

ALMs are immediate whereas citations are slow. If we assume for a moment that citations are a definitive means to determine the impact of a paper (which they may not be), then can ALMs predict citations? This would make them very useful in the evaluation of scientists and their endeavours. Unfortunately, this dataset is not sufficient to answer this properly, but with multiple timepoints, the question could be investigated. I looked at number of paper downloads and also the Mendeley score to see how these two things may foretell citations. What follows is a strategy to do this is an unbiased way with few confounders.

scopus v citesThe dataset has a Scopus column, but for some reason these data are incomplete. It is possible to download data (but not on this scale AFAIK) for citations from Web of Science and then use the DOI to cross-reference to the other dataset. This plot shows the Scopus data as a function of “Total Citations” from Web of Science, for 500 papers. I went with the Web of Science data as this appears more robust.

The question is whether there is a relationship between downloads of a paper (Counter, either PDF or HTML) and citations. Or between Mendeley score and citations. I figured that downloading, Mendeley and citation, show three progressive levels of “commitment” to a paper and so they may correlate differently with citations. Now, to look at this for all PLoS journals for all time would be silly because we know that citations are field-specific, journal-specific, time-sensitive etc. So I took the following dataset from Web of Science: the top 500 most-cited papers in PLoS ONE for the period of 2007-2010 limited to “cell biology”. By cross-referencing I could check the corresponding values for Counter and for Mendeley.

CounterMendelyvsCites

I was surprised that the correlation was very weak in both cases. I thought that the correlation would be stronger with Mendeley, however signal-to-noise is a problem here with few users of the service compared with counting downloads. Below each plot is a ranked view of the papers, with the Counter or Mendeley data presented as a rolling average. It’s a very weak correlation at best. Remember that this is post-hoc. Papers that have been cited more would be expected to generate more views and higher Mendeley scores, but this is not necessarily so. Predicting future citations based on Counter or Mendeley, will be tough. To really know if this is possible, this approach needs to be used with multiple ALM timepoints to see if there is a predictive value for ALMs, but based on this single timepoint, it doesn’t seem as though prediction will be possible.

Again, looking at this for a closed access journal would be very interesting. The most-downloaded paper in this set, had far more views (143,952) than other papers cited a similar number of times (78). The paper was this one which I guess is of interest to bodybuilders! Presumably, it was heavily downloaded by people who probably are not in a position to cite the paper. Although these downloads didn’t result in extra citations, this paper has undeniable impact outside of academia. Because PLoS is open access, the bodybuilders were able to access the paper, rather than being met by a paywall. Think of the patients who are trying to find out more about their condition and can’t read any of the papers… The final point here is that ALMs have their own merit, irrespective of citations, which are the default metric for judging the impact of our work.

Methods: To crunch the numbers for yourself, head over to Figshare and download the csv. A Web of Science subscription is needed for the citation data. All the plots were generated in IgorPro, but no programming is required for these comparisons and everything I’ve done here can be easily done in Excel or another package.

Edit: Matt Hodgkinson (@mattjhodgkinson) Snr Ed at PLoS ONE told me via Twitter that all ALM data (periodically updated) are freely available here. This means that some of the analyses I wrote about are possible.

The post title comes from Six Plus One a track on Dad Man Cat by Corduroy. Plus is as close to PLoS as I could find in my iTunes library.

You Know My Name (Look Up The Number)

What is your h-index on Twitter?

This thought crossed my mind yesterday when I saw a tweet that was tagged #academicinsults

It occurred to me that a Twitter account is a kind of micro-publishing platform. So what would “publication metrics” look like for Twitter? Twitter makes analytics available, so they can easily be crunched. The main metrics are impressions and engagements per tweet. As I understand it, impressions are the number of times your tweet is served up to people in their feed (boosted by retweets). Engagements are when somebody clicks on the tweet (either a link or to see the thread or whatever). In publication terms, impressions would equate to people downloading your paper and engagements mean that they did something with it, like cite it. This means that a “h-index” for engagements can be calculated with these data.

For those that don’t know, the h-index for a scientist means that he/she has h papers that have been cited h or more times. The Twitter version would be a tweeter that has h tweets that were engaged with h or more times. My data is shown here:

TwitterAnalyticsMy twitter h-index is currently 36. I have 36 tweets that have been engaged with 36 or more times.

So, this is a lot higher than my actual h-index, but obviously there are differences. Papers accrue citations as time goes by, but the information flow on Twitter is so fast that tweets don’t accumulate engagement over time. In that sense, the Twitter h-index is less sensitive to the time a user has been active on Twitter, versus the real h-index which is strongly affected by age of the scientist. Other differences include the fact that I have “published” thousands of tweets and only tens of papers. Also, whether or not more people read my tweets compared to my papers… This is not something I want to think too much about, but it would affect how many engagements it is possible to achieve.

The other thing I looked at was whether replying to somebody actually means more engagement. This would skew the Twitter h-index. I filtered tweets that started with an @ and found that this restricts who sees the tweet, but doesn’t necessarily mean more engagement. Replies make up a very small fraction of the h tweets.

I’ll leave it to somebody else to calculate the Impact Factor of Twitter. I suspect it is very low, given the sheer volume of tweets.

Please note this post is just for fun. Normal service will (probably) resume in the next post.

Edit: As pointed out in the comments this post is short on “Materials and Methods”. If you want to calculate your ownTwitter h-index, go here. When logged in to Twitter, the analytics page should present your data (it may take some time to populate this page after you first view it). A csv can be downloaded from the button on the top-right of the page. I imported this into IgorPro (as always) to generate the plots. The engagements data need to be sorted in descending order and then the h-index can be found by comparing the numbers with their ranked position.

The post title is from the quirky B-side to the Let It Be single by The Beatles.

Strange Things – update

My post on the strange data underlying the new impact factor for eLife was read by many people. Thanks for the interest and for the comments and discussion that followed. I thought I should follow up on some of the issues raised in the post.

To recap:

  1. eLife received a 2013 Impact Factor despite only publishing 27 papers in the last three months of the census window. Other journals, such as Biology Open did not.
  2. There were spurious miscites to papers before eLife published any papers. I wondered whether this resulted in an early impact factor.
  3. The Web of Knowledge database has citations from articles in the past referring to future articles!

1. Why did eLife get an early Impact Factor? It turns out that there is something called a partial Impact Factor.  This is where an early Impact Factor is awarded to some journals in special cases. This is described here in a post at Scholarly Kitchen. Cell Reports also got an early Impact Factor and Nature Methods got one a few years ago (thanks to Daniel Evanko for tweeting about Nature Methods’ partial Impact Factor). The explanation is that if a journal is publishing papers that are attracting large numbers of citations it gets fast-tracked for an Impact Factor.

2. In a comment, Rafael Santos pointed out that the miscites were “from a 2013 eLife paper to an inexistent 2010 eLife paper, and another miscite from a 2013 PLoS Computational Biology paper to an inexistent 2011 eLife paper”. The post at Scholarly Kitchen confirms that citations are not double-checked or cleaned up at all by Thomson-Reuters. It occurred to me that journals looking to game their Impact Factor could alter the year for citations to papers in their own journal in order to inflate their Impact Factor. But no serious journal would do that – or would they?

3. This is still unexplained. If anybody has any ideas (other than time travel) please leave a comment.

Strange Things

I noticed something strange about the 2013 Impact Factor data for eLife.

Before I get onto the problem. I feel I need to point out that I dislike Impact Factors and think that their influence on science is corrosive. I am a DORA signatory and I try to uphold those principles. I admit that, in the past, I used to check the new Impact Factors when they were released, but no longer. This year, when the 2013 Impact Factors came out I didn’t bother to log on to take a look. A chance Twitter conversation with Manuel Théry (@ManuelTHERY) and Christophe Leterrier (@christlet) was my first encounter with the new numbers.

Huh? eLife has an Impact Factor?

For those that don’t know, the 2013 Impact Factor is worked out by counting the total number of 2013 cites to articles in a given journal that were published in 2011 and 2012. This number is divided by the number of “citable items” in that journal in 2011 and 2012.

Now, eLife launched in October 2012. So it seems unfair that it gets an Impact Factor since it only published papers for 12.5% of the window under scrutiny. Is this normal?

I looked up the 2013 Impact Factor for Biology Open, a Company of Biologists journal that launched in January 2012* and… it doesn’t have one! So why does eLife get an Impact Factor but Biology Open doesn’t?**

elife-JIFLooking at the numbers for eLife revealed that there were 230 citations in 2013 to eLife papers in 2011 and 2012. One of which was a mis-citation to an article in 2011. This article does not exist (the next column shows that there were no articles in 2011). My guess is that Thomson Reuters view this as the journal existing for 2011 and 2012, and therefore deserving of an Impact Factor. Presumably there are no mis-cites in the Biology Open record and it will only get an Impact Factor next year. Doesn’t this call into question the veracity of the database? I have found other errors in records previously (see here). I also find it difficult to believe that no-one checked this particular record given the profile of eLife.

elfie-citesPerhaps unsurprisingly, I couldn’t track down the rogue citation. I did look at the cites to eLife articles from all years in Web of Science, the Thomson Reuters database (which again showed that eLife only started publishing in Oct 2012). As described before there are spurious citations in the database. Josh Kaplan’s eLife paper on UNC13/Tomosyn managed to rack up 5 citations in 2004, some 9 years before it was published (in 2013)! This was along with nine other papers that somehow managed to be cited in 2004 before they were published. It’s concerning enough that these data are used for hiring, firing and funding decisions, but if the data are incomplete or incorrect this is even worse.

Summary: I’m sure the Impact Factor of eLife will rise as soon as it has a full window for measurement. This would actually be 2016 when the 2015 Impact Factors are released. The journal has made it clear in past editorials (and here) that it is not interested in an Impact Factor and won’t promote one if it is awarded. So, this issue makes no difference to the journal. I guess the moral of the story is: don’t take the Impact Factor at face value. But then we all knew that already. Didn’t we?

* For clarity, I should declare that we have published papers in eLife and Biology Open this year.

** The only other reason I can think of is that eLife was listed on PubMed right away, while Biology Open had to wait. This caused some controversy at the time. I can’t see why a PubMed listing should affect Impact Factor. Anyhow, I noticed that Biology Open got listed in PubMed by October 2012, so in the end it is comparable to eLife.

Edit: There is an update to this post here.

Edit 2: This post is the most popular on Quantixed. A screenshot of visitors’ search engine queries (Nov 2014)…

searches

The post title is taken from “Strange Things” from Big Black’s Atomizer LP released in 1986.

Vitamin K

Note: this is not a serious blog post.

Neil Hall’s think piece in Genome Biology on the Kardashian index (K-index) caused an online storm recently, spawning hashtags and outrage in not-so-equal measure. Despite all the vitriol that headed Neil’s way, very little of it concerned his use of Microsoft Excel to make his plot of Twitter followers vs total citations! Looking at the plot with the ellipse around a bunch of the points and also at the equations, I thought it might be worth double-checking Neil’s calculations.

In case you don’t know what this is about: the K-index is the ratio of actual Twitter followers (\(F_{a}\)) to the number of Twitter followers you are predicted to have (\(F_{c}\)) based on the total number of citations to your papers (\(C\)) from the equation:

\(F_{c}=43.3C^{0.32} \)

So the K-index is:

\(K-index=\frac{F_{a}}{F_{c}}\)

He argues that if a scientist has a K-index >5 then they are more famous for their twitterings than for their science. This was the most controversial aspect of the piece. It wasn’t clear whether he meant that highly cited scientists should get tweeting or that top-tweeters should try to generate some more citations (not as easy as it sounds). The equation for \(F_{c}\) was a bit suspect, derived from some kind of fit through some of the points. Anyway, it seemed to me that the ellipse containing the Kardashians didn’t look right.

I generated the data for \(F_{c}\) and for a line to show the threshold at which one becomes a Kardashian (k) in IgorPro as follows:

Make /o /N=100000 fc
fc =43.3*(x^0.32)
Duplicate fc k //yes, this does look rude
k *=5
display fc, k //and again!

K-index

This plot could be resized and overlaid on Neil’s Excel chart from Genome Biology. I kept the points but deleted the rest and then made this graph.

The Kardashians are in the peach zone. You’ll notice one poor chap is classed as a Kardashian by Neil, yet he is innocent! Clearly below the line, i.e. K-index <5.

Two confessions:

  1. My K-index today is 1.97 according to Twitter and Google Scholar.
  2. Embarrassingly, I didn’t know of the business person who gave her name to the K-index was until reading Neil’s article and the ensuing discussion. So I did learn something from this!

The post title is taken from “Vitamin K” by Gruff Rhys from the Hotel Shampoo album.

“Yeah” Is What We Had

When it comes to measuring the impact of our science, citations are pretty much all we have. And not only that but they only say one thing – yeah – with no context. How can we enrich citation data?

Much has been written about how and why and whether or not we should use metrics for research assessment. If we accept that metrics are here to stay in research assessment (of journals, Universities, departments and of individuals), I think we should be figuring out better ways to look at the available information.

8541947962_6853dd9786_zCitations to published articles are the key metric under discussion. This is because they are linked to research outputs (papers), have some relation to “impact” and they can be easily computed and a number of metrics have been developed to draw out information from the data (H-index, IF etc.). However there are many known problems with citations such as: they are heavily influenced by the size of the field. What I want to highlight here is what a data-poor resource they are and think of ways we could enrich the dataset with minimal modification to our existing databases.

1. We need a way to distinguish a yeah from a no

The biggest weakness of using citations as a measure of research impact is that a citation is a citation. It just says +1. We have no idea if +1 means “the paper stinks” or “the work is amazing!”.  It’s incredible that we can rate shoelaces on Amazon or eBay but we haven’t figured out a way to do this for scientific papers. Here’s a suggestion:

  • A neutral citation is +1
  • A positive citation is +2
  • A negative citation is -1

A neutral citation would be stating a fact and adding reference to support it, e.g. DNA is a double helix (Watson & Crick, 1953).

A positive citation would be something like: in agreement with Bloggs et al. (2010), we also find x.

A negative citation might be: we have tested the model proposed by Smith & Jones (1977) and find that it does not hold.

One further idea (described here) is to add more context to citation using keywords. Such as “replicating”, “using”, “consistent with”. This would also help with searching the scientific literature.

2. Multiple citations in one article

Because currently, citations are a +1, there is no way to distinguish whether the paper giving the citation was mentioning the cited paper in passing or was entirely focussed on that one paper.

Another way to think about this is that there are multiple reasons to cite a paper: maybe the method or reagent is being used, maybe they are talking about Figure 2 showing X or Figure 5 showing Y. What if a paper is talking about all of these things? In other words, the paper was very useful. Shouldn’t we record that interest?

Suggestion: A simple way to do this is to count the number of mentions in the text of the paper rather than just if the paper appears in the reference list.

3. Division of a citation unit for fair credit to each author

Calculations such as the H-index make no allowance for the position of the author in the author list (used in biological sciences and some other fields to denote contribution to the paper). It doesn’t make sense that the 25th author on a 50 author paper receives 100% of the citation credit as the first or last author. Similarly, the first author on a two author paper is only credited in the same way as the middle author on a multi-author paper. The difference in contribution is clear, but the citation credit is not. This is because the citation credit for the former paper is worth 25 times that of the latter! This needs to be equalised. The citation unit, c could be divided to achieve fair credit for authors. At the moment, c=1, but could be multiples (or negative values) as described above. Here’s a suggestion:

  • First (and multiple first) and last (and co-last) authors get 0.5c divided by number of authors.
  • The remainder, 0.5c, is divided between all authors.

For a two author paper: first author gets 0.5c and last author gets 0.5c. (0.5c/2+0.5c/2)=0.5c

For a ten author paper with one first author and one last author, first and last author each get (0.5c/2+0.5c/10)=0.3c and the 5th author gets (0c+0.5c/10)=0.05c.

Note that the sum for all authors will equal c. So this is equalised for all papers. These citation credits would then be the basis for H-index and other calculations for individuals.

Most simply, the denominator would be the number of authors, or – if we can figure out a numerical credit system – each author could be weighted according to their contribution.

4. Citations to reviews should be downgraded

A citation to a review is not equal to a citation to a research paper. For several reasons. First, they are cited at a higher rate, because they are a handy catchall citation particularly for the Introduction section in papers. This isn’t fair either and robs credit from the people who did the work that actually demonstrated what is being discussed. Second, the achievement of publishing a review is nothing in comparison to publishing a paper. Publishing a review involves 1) being asked, 2) writing it, 3) light peer review and some editing and that’s it! Publishing a research paper involves much more effort: having the idea, getting the money, hiring the people, training the people, getting a result – and we are only at the first panel in Fig 1A. Not to mention the people-hours and arduous peer review process. It’s not fair that citations to reviews are treated as equal to papers when it comes to research assessment.

Suggestion: a citation to a review should be worth a fraction (maybe 1/10th) of a citation to a research paper.

In addition, there are too many reviews written at the moment. I think this is not because they are particularly useful. Very few actually contribute a new view or new synthesis of an area, most are just a summary of the area. Journals like them because they drive up their citation metrics. Authors like them because it is nice to be invited to write something – it means people are interested in what you have to say… If citations to reviews were downgraded, there would be less incentive to publish them and we would have more space for all those real papers that are getting rejected at journals that claim that space is a limitation for publication.

5. Self-citations should be eliminated

If we are going to do all of the above, then self-citation would pretty soon become a problem. Excessive self-citation would be difficult to police, and not many scientists would go for a -1 citation to their own work. So, the simplest thing to do is to eliminate self-citation. Author identification is crucial here. At the moment this doesn’t work well. In ISI and Scopus, whatever algorithm they use keeps missing some papers of mine (and my name is not very common at all). I know people who have been grouped with other people that they have published one or two papers with. For authors with ambiguous names, this is a real problem. ORCID is a good solution and maybe having an ORCID (or similar) should be a requirement for publication in the future.

Suggestion: the company or body that collates citation information needs to accurately assign authors and make sure that research papers are properly segregated from reviews and other publication types.

These were five things I thought of to enrich citation data to improve research assessment, do you have any other ideas?

The post title is taken from ‘”Yeah” Is What We Had’ by Grandaddy from their album Sumday.

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.