My Favorite Things

I realised recently that I’ve maintained a consistent iTunes library for ~10 years. For most of that time I’ve been listening exclusively to iTunes, rather than to music in other formats. So the library is a useful source of information about my tastes in music. It should be possible to look at who are my favourite artists, what bands need more investigation, or just to generate some interesting statistics based on my favourite music.

Play count is the central statistic here as it tells me how often I’ve listened to a certain track. It’s the equivalent of a +1/upvote/fave/like or maybe even a citation. Play count increases by one if you listen to a track all the way to the end. So if a track starts and you don’t want to hear it and you skip on to the next song, there’s no +1. There’s a caveat here in that the time a track has been in the library, influences the play count to a certain extent – but that’s for another post*. The second indicator for liking a track or artist is the fact that it’s in the library. This may sound obvious, but what I mean is that artists with lots of tracks in the library are more likely to be favourite artists compared to a band with just one or two tracks in there. A caveat here is that some artists do not have long careers for a variety of reasons, which can limit the number of tracks actually available to load into the library. Check the methods at the foot of the post if you want to do the same.

What’s the most popular year? Firstly, I looked at the most popular year in the library. This question was the focus of an earlier post that found that 1971 was the best year in music. The play distribution per year can be plotted together with a summary of how many tracks and how many plays in total from each year are in the library. There’s a bias towards 90s music, which probably reflects my age, but could also be caused by my habit of collecting CD singles which peaked as a format in this decade. The average number of plays is actually pretty constant for all years (median of ~4), the mean is perhaps slightly higher for late-2000s music.

Favourite styles of music: I also looked at Genre. Which styles of music are my favourite? I plotted the total number of tracks versus the total number of plays for each Genre in the library. Size of the marker reflects the median number of plays per track for that genre. Most Genres obey a rule where total plays is a function of total tracks, but there are exceptions. Crossover, Hip-hop/Rap and Power-pop are highlighted as those with an above average number of plays. I’m not lacking in Power-pop with a few thousand tracks, but I should probably get my hands on more Crossover or Hip-Hop/Rap.

Nov14

Using citation statistics to find my favourite artists: Next, I looked at who my favourite artists are. It could be argued that I should know who my favourite artists are! But tastes can change over a 10 year period and I was interested in an unbiased view of my favourite artists rather than who I think they are. A plot of Total Tracks vs Mean plays per track is reasonably informative. The artists with the highest plays per track are those with only one track in the library, e.g. Harvey Danger with Flagpole Sitta. So this statistic is pretty unreliable. Equally, I’ve got lots of tracks by Manic Street Preachers but evidently I don’t play them that often. I realised that the problem of identifying favourite artists based on these two pieces of information (plays and number of tracks) is pretty similar to assessing scientists using citation metrics (citations and number of papers). Hirsch proposed the h-index to meld these two bits of information into a single metric, the h-index. It’s easily computed and I already had an Igor procedure to calculate it en masse, so I ran it on the library information.

Before doing this, I consolidated multiple versions of the same track into one. I knew that I had several versions of the same track, especially as I have multiple versions of some albums (e.g. Pet Sounds = 3 copies = mono + stereo + a capella), the top offending track was “Baby’s Coming Back” by Jellyfish, 11 copies! Anyway, these were consolidated before running the h-index calculation.

The top artist was Elliott Smith with an h-index of 32. This means he has 32 tracks that have been listened to at least 32 times each. I was amazed that Muse had the second highest h-index (I don’t consider myself a huge fan of their music) until I remembered a period where their albums were on an iPod Nano used during exercise. Amusingly (and narcissistically) my own music – the artist names are redacted – scored quite highly with two out of three bands in the top 100, which are shown here. These artists with high h-indeces are the most consistently played in the library and probably constitute my favourite artists, but is the ranking correct?

The procedure also calculates the g-index for every artist. The g-index is similar to the h-index but takes into account very highly played tracks (very highly cited papers) over the h threshold. For example, The Smiths h=26. This could be 26 tracks that have been listened to exactly 26 times or they could have been listened to 90 times each. The h-index cannot reveal this, but the g-index gets to this by assessing average plays for the ranked tracks. The Smiths g=35. To find the artists that are most-played-of-the-consistently-most-played, I subtracted h from g and plotted the Top 50. This ranked list I think most closely represents my favourite artists, according to my listening habits over the last ten years.

Nov14-2

Track length: Finally, I looked at the track length. I have a range of track lengths in the library, from “You Suffer” by Napalm Death (iTunes has this at 4 s, but Wikipedia says it is 1.36 s), through to epic tracks like “Blue Room” by The Orb. Most tracks are in the 3-4 min range. Plays per track indicates that this track length is optimal with most of the highly played tracks being within this window. The super-long tracks are rarely listened to, probably because of their length. Short tracks also have higher than average plays, probably because they are less likely to be skipped, due to their length.

These were the first things that sprang to mind for iTunes analysis. As I said at the top, there’s lots of information in the library to dig through, but I think this is enough for one post. And not a pie-chart in sight!

Methods: the library is in xml format and can be read/parsed this way. More easily, you can just select the whole library and copy-paste it into TextEdit and then load this into a data analysis package. In this case, IgorPro (as always). Make sure that the interesting fields are shown in the full library view (Music>Songs). To do everything in this post you need artist, track, album, genre, length, year and play count. At the time of writing, I had 21326 tracks in the library. For the “H-index” analysis, I consolidated multiple versions of the same track, giving 18684 tracks. This is possible by concatenating artist and the first ten characters of the track title (separated by a unique character) and adding the play counts for these concatenated versions. The artist could then be deconvolved (using the unique character) and used for the H-calculation. It’s not very elegant, but seemed to work well. The H-index and G-index calculations were automated (previously sort-of-described here), as was most of the plot generation. The inspiration for the colour coding is from the 2013 Feltron Report.

* there’s an interesting post here about modelling the ideal playlist. I worked through the ideas in that post but found that it doesn’t scale well to large libraries, especially if they’ve been going for a long time, i.e. mine.

The post title is taken from John Coltrane’s cover version of My Favorite Things from the album of the same name. Excuse the US English spelling.

Belly Button Window

A bit of navel gazing for this post. Since moving the blog to wordpress.com in the summer, it recently accrued 5000 views. Time to analyse what people are reading…

blogstatsThe most popular post on the blog (by a long way) is “Strange Things“, a post about the eLife impact factor (2824 views). The next most popular is a post about a Twitter H-index, with 498 views. The Strange Things post has accounted for ~50% of views since it went live (bottom plot) and this fraction seems to be creeping up. More new content is needed to change this situation.

I enjoy putting blog posts together and love the discussion that follows from my posts. It’s also been nice when people have told me that they read my blog and enjoy my posts. One thing I didn’t expect was the way that people can take away very different messages from the same post. I don’t know why I found this surprising, since this often happens with our scientific papers! Actually, in the same way as our papers, the most popular posts are not the ones that I would say are the best.

Wet Wet Wet: I have thought about deleting the Strange Things post, since it isn’t really what I want this blog to be about. An analogy here is the Scottish pop-soul outfit Wet Wet Wet who released a dreadful cover of The Troggs’ “Love is All Around” in 1994. In the end, the band deleted the single in the hope of redemption, or so they said. Given that the song had been at number one for 15 weeks, the damage was already done. I think the same applies here, so the post will stay.

Directing Traffic: Most people coming to the blog are clicking on links on Twitter. A smaller number come via other blogs which feature links to my posts. A very small number come to the blog via a Google search. Google has changed the way it formats the clicks and so most of the time it is not possible to know what people were searching for. For those that I can see, the only search term is… yes, you’ve guessed it: “elife impact factor”.

Methods: WordPress stats are available for blog owners via URL formatting. All you need is your API key and (obviously) your blog address.

Instructions are found at http://stats.wordpress.com/csv.php

A basic URL format would be: http://stats.wordpress.com/csv.php?api_key=yourapikey&blog_uri=yourblogaddress replacing yourapikey with your API key (this can be retrieved at https://apikey.wordpress.com) and yourblogaddress with your blog address e.g. quantixed.wordpress.com

Various options are available from the first page to get the stats in which you are  interested. For example, the following can be appended to the second URL to get a breakdown of views by post title for the past year:

&table=postviews&days=365&limit=-1

The format can be csv, json or xml depending on how your preference for what you want to do next with the information.

The title is from “Belly Button Window” by Jimi Hendrix, a posthumous release on the Cry of Love LP.

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.

Tips from the Blog II

An IgorPro tip this week. The default font for plots is Geneva. Most of our figures are assembled using Helvetica for labelling. The default font can be changed in Igor Graph Preferences, but Preferences need to be switched on in order to be implemented. Anyway, I always seem to end up with a mix of Geneva plots and Helevetica plots. This can be annoying as the fonts are pretty similar yet the spacing is different and this can affect the plot size. Here is a quick procedure Helvetica4All() to rectify this for all graph windows.

Helvetica4All

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.

Round and Round

I thought I’d share a procedure for rotating a 2D set of coordinates about the origin. Why would you want do this? Well, we’ve been looking at cell migration in 2D – tracking nuclear position over time. Cells migrate at random and I previously blogged about ways to visualise these tracks more clearly. Part of this earlier procedure was to set the start of each track at (0,0). This gives a random hairball of tracks moving away from the origin. Wouldn’t it be a good idea to orient all the tracks so that the endpoint lies on the same axis? This would simplify the view and allow one to assess how ‘directional’ the cell tracks are. To rotate a set of coordinates, you need to use a rotation matrix. This allows you to convert the x,y coordinates to their new position x’,y’. This rotation is counter-clockwise.

\(x’ = x \cos \theta – y \sin \theta\,\)

\(y’ = x \sin \theta + y \cos \theta\,\)

However, we need to find theta first. To do this we need to find the angle between two lines, using this formula.

\(\cos \theta = \frac {\mathbf a \cdot \mathbf b}{\left \Vert {\mathbf a} \right \Vert \cdot \left \Vert {\mathbf b} \right \Vert} \)

The maths is kept to a minimum here. If you are interested, look at the code at the bottom.

beforeThe two lines (a and b) are formed by the x-axis (origin to some point on the x-axis, i.e. y=0) and by a line running from the origin to the last coordinate in the series. This calculation can be done for each track with theta for each track being used to rotate the that whole track (x,y changed to x’,y’ for each point).

Here is an example of just a few tracks from an experiment. Typically we have hundreds of tracks for each experimental group and the code will blast through them all very quickly (<1 s).

after

After rotation, the tracks are now aligned so that the last point is on the x-axis at y=0. This allows us to see how ‘directional’ the tracks are. The end points are now aligned, when they migrated there, how convoluted was their path.

The code to do this is up on Igor Exchange code snippets. A picture of the code is below (markup for code in WordPress is not very clear). See the code snippet if you want to use it.

rotator

The weakness of this method is that acos (arccos) only gives results from 0 to Pi (0 to 180°). There is a correction in the procedure, but everything needs editing if you want to rotate the co-ordinates to some other plane. Feedback welcome.

Edit Jim Prouty and A.G. have suggested two modifications to the code. The first is to use complex waves rather than 2D real waves. Then use two native Igor functions r2polar or p2rect. The second suggestion is to use Matrix operations! As is often the case with Igor there are several ways of doing things. The method described here is long-winded compared to a MatrixOp and if the waves were huge these solutions would be much, much faster. As it is, our migration movies typically have 60 points and as mentioned rotator() blasts through them very quickly. More complex coordinate sets would need something more sophisticated.

The post title is taken from “Round & Round” by New Order from their Technique LP.

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.

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.