More on the theme of “The Digital Cell“: using quantitative, computational approaches in cell biology.
So you want to get started? Well, the short version of this post is:
Find something that you need to automate and get going!
I make no claim to be a computer wizard. My first taste of programming was the same as anyone who went to school in the UK in the 1980s: BBC Basic. Although my programming only went as far as copying a few examples from the book (right), this experience definitely reduced the “fear of the command line”. My next encounter with coding was to learn HTML when I was an undergraduate. It was not until I was a postdoc that I realised that I needed to write scripts in order get computers to do what I wanted them to do for my research.
I work in cell biology. My work involves a lot of microscopy. From the start, I used computer-based methods to quantify images. My first paper mentions quantifying images, but it wasn’t until I was a PhD student that I first used NIH Image (as it was called then) to extract quantitative information from confocal micrographs. I was also introduced to IgorPRO (version 3!) as a PhD student, but did no programming. That came later. As a postdoc, we used Scanalytics’ IPLab and Igor (as well as a bit of ImageJ as it had become). IPLab had an easy scripting language and it was in this program that I learned to write macros for analysis. At this time there were people in the lab who were writing software in IgorPro and MATLAB. While I didn’t pick up programming in IgorPRO or MATLAB then, it made me realise what was possible.
When I started my own group I discovered that IPLab had been acquired by BD Biosciences and then stripped out. I had hundreds of useless scripts and needed a new solution. ImageJ had improved enormously by this time and so this became our default image analysis program. The first data analysis package I bought was IgorPro (version 6) and I have stuck with it since then. In a future post, I will probably return to whether or not this was a good path.
Getting started with programming
Around 2009, I was still unable to program properly. I needed a macro for baseline subtraction – something really simple – and realised I didn’t know how to do it. We didn’t have just one or two traces to modify, we had hundreds. This was simply not possible by hand. It was this situation that made me realise I needed to learn to program.
…having a concrete problem that is impossible to crack any other way is the best motivator for learning to program.
This might seem obvious, but having a concrete problem that is impossible to crack any other way is the best motivator for learning to program. I know many people who have decided they “want to learn to code” or they are “going to learn to use R”. This approach rarely works. Sitting down and learning this stuff without sufficient motivation is really tough. So I would advise someone wanting to learn programming to find something that needs automation and just get going. Just get something to work!
Don’t worry (initially) about any of the following:
- What program/language to use – as long as it is possible, just pick something and do it
- If your code is ugly or embarrassing to show to an expert – as long as it runs, it doesn’t matter
- About copy-and-pasting from examples – it’s OK as long as you take time to understand what you are doing, this is a quick way to make progress. Resources such as stackoverflow are excellent for this
- Bugs – you can squish them, they will frustrate you, but you might need some…
- Help – ask for help. Online forums are great, experts love showing off their knowledge. If you have local expertise, even better!
Once you have written something (and it works)… congratulations, you are a computer programmer!
Seriously, that is all there is to it. OK, it’s a long way to being a good programmer or even a competent one, but you have made a start. Like Obi Wan Kenobi says: you’ve taken your first step into a larger world.
So how do you get started with an environment like IgorPro? This will be the topic for next time.
Part of a series on the future of cell biology in quantitative terms.
If you are a cell biologist, you will have noticed the change in emphasis in our field.
At one time, cell biology papers were – in the main – qualitative. Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed. More recently, as technology advanced, computing power increased and data sets became more complex, we have seen larger scale analysis, modelling, and automation begin to take centre stage.
This change in emphasis encompasses several areas including (in no particular order):
- Statistical analysis
- Image analysis
- Automation allowing analysis at scale
- Version control
- Data storage, archiving and accessing large datasets
- Electronic lab notebooks
- Computer vision and machine learning
- Prospective and retrospective modelling
- Mathematics and physics
The application of these areas is not new to biology and has been worked on extensively for years in certain areas. Perhaps most obviously by groups that identified themselves as “systems biologists”, “computational biologists”, and people working on large-scale cell biology projects. My feeling is that these methods have now permeated mainstream (read: small-scale) cell biology to such an extent that any groups that want to do cell biology in the future have to adapt in order to survive. It will change the skills that we look for when recruiting and it will shape the cell biologists of the future. Other fields such as biophysics and neuroscience are further through this change, while others have yet to begin. It is an exciting time to be a biologist.
I’m planning to post occasionally about the way that our cell biology research group is working on these issues: our solutions and our problems.
Part of a series on the future of cell biology in quantitative terms.
A few days ago, Retraction Watch published the top ten most-cited retracted papers. I saw this post with a bar chart to visualise these citations. It didn’t quite capture what the effect (if any) a retraction has on citations. I thought I’d quickly plot this out for the number one article on the list.
The plot is pretty depressing. The retraction has no effect on citations. Note that the retraction notice has racked up 125 citations, which could mean that at least some of the ~1000 citations to the original article that came after the retraction, acknowledge the fact that the article has been pulled.
The post title is taken from “What Difference Does it Make?” by The Smiths from ‘The Smiths’ and ‘Hatful of Hollow’
bioRxiv, the preprint server for biology, recently turned 2 years old. This seems a good point to take a look at how bioRxiv has developed over this time and to discuss any concerns sceptical people may have about using the service.
Firstly, thanks to Richard Sever (@cshperspectives) for posting the data below. The first plot shows the number of new preprints deposited and the number that were revised, per month since bioRxiv opened in Nov 2013. There are now about 200 preprints being deposited per month and this number will continue to increase. The cumulative article count (of new preprints) shows that, as of the end of last month, there are >2500 preprints deposited at bioRxiv.
What is take up like across biology? To look at this, the number of articles in different subject categories can be totted up. Evolutionary Biology, Bioinformatics and Genomics/Genetics are the front-running disciplines. Obviously counting articles should be corrected for the size of these fields, but it’s clear that some large disciplines have not adopted preprinting in the same way. Cell biology, my own field, has some catching up to do. It’s likely that this reflects cultures within different fields. For example, genomics has a rich history of data deposition, sharing and openness. Other fields, less so…
So what are we waiting for?
I’d recommend that people wondering about preprinting go and read Stephen Curry’s post “just do it“. Any people who remain sceptical should keep reading…
Do I really want to deposit my best work on bioRxiv?
I’ve picked six preprints that were deposited in 2015. This selection demonstrates how important work is appearing first at bioRxiv and is being downloaded thousands of times before the papers appear in the pages of scientific journals.
- Accelerating scientific publishing in biology. A preprint about preprinting from Ron Vale, subsequently published in PNAS.
- Analysis of protein-coding genetic variation in 60,706 humans. A preprint summarising a huge effort from ExAC Exome Aggregation Consortium. 12,366 views, 4,534 downloads.
- TP53 copy number expansion correlates with the evolution of increased body size and an enhanced DNA damage response in elephants. This preprint was all over the news, e.g. Science.
- Sampling the conformational space of the catalytic subunit of human γ-secretase. CryoEM is the hottest technique in biology right now. Sjors Scheres’ group have been at the forefront of this revolution. This paper is now out in eLife.
- The genome of the tardigrade Hypsibius dujardini. The recent controversy over horizontal gene transfer in Tardigrades was rapidfire thanks to preprinting.
- CRISPR with independent transgenes is a safe and robust alternative to autonomous gene drives in basic research. This preprint concerning biosafety of CRISPR/Cas technology could be accessed immediately thanks to preprinting.
But many journals consider preprints to be previous publications!
Wrong. It is true that some journals have yet to change their policy, but the majority – including Nature, Cell and Science – are happy to consider manuscripts that have been preprinted. There are many examples of biology preprints that went on to be published in Nature (ancient genomes) and Science (hotspots in birds). If you are worried about whether the journal you want to submit your work to will allow preprinting, check this page first or the SHERPA/RoMEO resource. The journal “information to authors” page should have a statement about this, but you can always ask the Editor.
I’m going to get scooped
Preprints establish priority. It isn’t possible to be scooped if you deposit a preprint that is time-stamped showing that you were the first. The alternative is to send it to a journal where no record will exist that you submitted it if the paper is rejected, or sometimes even if they end up publishing it (see discussion here). Personally, I feel that the fear of scooping in science is overblown. In fields that are so hot that papers are coming out really fast the fear of scooping is high, everyone sees the work if its on bioRxiv or elsewhere – who was first is clear to all. Think of it this way: depositing a preprint at bioRxiv is just the same as giving a talk at a meeting. Preprints mean that there is a verifiable record available to everyone.
Preprints look ugly, I don’t want people to see my paper like that.
The depositor can format their preprint however they like! Check out Christophe Leterrier’s beautifully formatted preprint, or this one from Dennis Eckmeier. Both authors made their templates available so you can follow their example (1 and 2).
Yes but does -insert name of famous scientist- deposit preprints?
Lots of high profile scientists have already used bioRxiv. David Bartel, Ewan Birney, George Church, Ray Deshaies, Jennifer Doudna, Steve Henikoff, Rudy Jaenisch, Sophien Kamoun, Eric Karsenti, Maria Leptin, Rong Li, Andrew Murray, Pam Silver, Bruce Stillman, Leslie Vosshall and many more. Some sceptical people may find this argument compelling.
I know how publishing works now and I don’t want to disrupt the status quo
It’s paradoxical how science is all about pushing the frontiers, yet when it comes to publishing, scientists are incredibly conservative. Physics and Mathematics have been using preprinting as part of the standard route to publication for decades and so adoption by biology is nothing unusual and actually, we will simply be catching up. One vision for the future of scientific publishing is that we will deposit preprints and then journals will search out the best work from the server to highlight in their pages. The journals that will do this are called “overlay journals”. Sounds crazy? It’s already happening in Mathematics. Terry Tao, a Fields medal-winning mathematician recently deposited a solution to the Erdos discrepency problem on arXiv (he actually put them on his blog first). This was then “published” in Discrete Analysis, an overlay journal. Read about this here.
Disclaimer: other preprint services are available. F1000 Research, PeerJ Preprints and of course arXiv itself has quantitative biology section. My lab have deposited work at bioRxiv (1, 2 and 3) and I am an affiliate for the service, which means I check preprints before they go online.
Edit 14/12/15 07:13 put the scientists in alphabetical order. Added a part about scooping.
The post title comes from the term “white label” which is used for promotional vinyl copies of records ahead of their official release.
I saw this great tweet (fairly) recently:
I thought this was such a great explanation of when to submit your paper.
It reminded me of a diagram that I sketched out when talking to a student in my lab about a paper we were writing. I was trying to explain why we don’t exaggerate our findings. And conversely why we don’t undersell our results either. I replotted it below:
Getting out to review is a major hurdle to publishing a paper. Therefore, convincing the Editor that you have found out something amazing is the first task. This is counterbalanced by peer review, which scrutinises the claims made in a paper for their experimental support. So, exaggerated claims might get you over the first hurdle, but it will give you problems during peer review (and afterwards if the paper makes it to print). Conversely, underselling or not interpreting all your data fully is a different problem. It’s unlikely to impress the Editor as it can make your paper seem “too specialised”, although if it made it to the hands of your peers they would probably like it! Obviously at either end of the spectrum no-one likes a dull/boring/incremental paper and everyone can smell a rat if the claims are completely overblown, e.g. genome sequence of Sasquatch.
So this is why we try to interpret our results fully but are careful not to exaggerate our claims. It might not get us out to review every time, but at least we can sleep at night.
I don’t know if this is a fair representation. Certainly depending on the journal the scale of the y-axis needs to change!
The post title is taken from “Middle of the Road” by Teenage Fanclub a B-side from their single “I Don’t Want Control of You”.
Our most recent manuscript was almost ready for submission. We were planning to send it to an open access journal. It was then that I had the thought: how many papers in the reference list are freely available?
It somehow didn’t make much sense to point readers towards papers that they might not be able to access. So, I wondered if there was a quick way to determine how papers in my reference list were open access. I asked on twitter and got a number of suggestions:
- Search crossref to find out if the journal is in DOAJ (@epentz)
- How Open Is It? from Cottage Labs will check a list of DOIs (up to 20) for openness (@emanuil_tolev)
- Open access DOI Resolver will perform a similar task (@neurocraig)
I actually used a fourth method (from @biochemistries and @invisiblecomma) which was to use HubMed, although in the end a similar solution can be reached by searching PubMed itself. Whereas the other strategies will work for a range of academic texts, everything in my reference list was from PubMed. So this solution worked well for me. I pulled out the list of Accessions (PMIDs) for my reference list. This was because some papers were old and I did not have their DOIs. The quickest way to do this was to make a new EndNote style that only contained the field Accession and get it to generate a new bibliography from my manuscript. I appended
[uid] OR after each one and searched with that term.
My paper had 44 references. Of these, 35 were freely available to read. I was actually surprised by how many were available. So, 9 papers were not free to read. As advised, I checked each one to really make sure that the HubMed result was accurate, and it was.
Please note that I’d written the paper without giving this a thought and citing papers as I normally do: the best demonstration of something, the first paper to show something, using primary papers as far as possible.
Seven of the nine I couldn’t compromise on. They’re classic papers from 80s and 90s that are still paywalled but are unique in what they describe. However, two papers were reviews in closed access journals. Now these I could do something about! Especially as I prefer to cite the primary literature anyway. Plus, most reviews are pretty unoriginal in what they cover and an alternative open access version that is fairly recent can easily be found. I’ll probably run this check for future manuscripts and see what it throws up.
It’s often said that papers are our currency in science. The valuation of this currency comes from citations. Funnily enough, we the authors are in a position to actually do something about this. I don’t think any of us should compromise the science in our manuscripts. However, I think we could all probably pay a bit more attention to the citations that we dish out when writing a paper. Whether this is simply to make sure that what we cite is widely accessible, or just making sure that credit goes to the right people.
The post title is taken from “To Open Closed Doors” by D.R.I. from the Dirty Rotten LP
I thought I’d compile a list of songs related to biomedical science. These were all found in my iTunes library. I’ve missed off multiple entries for the same kind of thing, as indicated.
- Grand Mal -Elliott Smith from XO Sessions
- She’s Lost Control – Joy Division from Unknown Pleasures (Epilepsy)
- Aneuryism – Nirvana from Hormoaning EP
- Serotonin – Mansun from Six
- Serotonin Smile – Ooberman from Shorley Wall EP
- Brain Damage – Pink Floyd from Dark Side of The Moon
- Paranoid Schizophrenic – The Bats from How Pop Can You Get?
- Headacher – Bear Quartet from Penny Century
- Headache – Frank Black from Teenager of the Year
- Manic Depression – Jimi Hendrix Experience and lots of other songs about depression
- Paranoid – Black Sabbath from Paranoid (thanks to Joaquin for the suggestion!)
- Cancer (interlude) – Mansun from Six
- Hepatic Tissue Fermentation – Carcass or pretty much any song in this genre of Death Metal
- Whiplash – Metallica from Kill ‘Em All
- Another Invented Disease – Manic Street Preachers from Generation Terrorists
- Broken Nose – Family from Bandstand
- Bones – Radiohead from The Bends
- Ana’s Song – Silverchair from Neon Ballroom (Anorexia Nervosa)
- 4st 7lb – Manic Street Preachers from The Holy Bible (Anorexia Nervosa)
- November Spawned A Monster – Morrissey from Bona Drag (disability)
- Castles Made of Sand – Jimi Hendrix Experience from Axis: Bold As Love (disability)
- Cardiac Arrest – Madness from 7
- Blue Veins – The Raconteurs from Broken Boy Soldiers
- Vein Melter – Herbie Hancock from Headhunters
- Scoliosis – Pond from Rock Collection (curvature of the spine)
- Taste the Blood – Mazzy Star… lots of songs with blood in the title.
- Biotech is Godzilla – Sepultura from Chaos A.D.
- Luminol – Ryan Adams from Rock N Roll
- Feel Good Hit Of The Summer – Queens of The Stone Age from Rated R (prescription drugs of abuse)
- Stars That Play with Laughing Sam’s Dice – Jimi Hendrix Experience (and hundreds of other songs about recreational drugs)
- Tramazi Parti – Black Grape from It’s Great When You’re Straight…
- Z is for Zofirax – Wingtip Sloat from If Only For The Hatchery
- Goldfish and Paracetamol – Catatonia from International Velvet
- L Dopa – Big Black from Songs About Fucking
Genetics and molecular biology
- Genetic Reconstruction – Death from Spiritual Healing
- Genetic – Sonic Youth from 100%
- Hair and DNA – Hot Snakes from Audit in Progress
- DNA – Circle from Meronia
- Biological – Air from Talkie Walkie
- Gene by Gene – Blur from Think Tank
- My Selfish Gene – Catatonia from International Velvet
- Sheer Heart Attack – Queen (“it was the DNA that made me this way”)
- Mutantes – Os Mutantes
- The Missing Link – Napalm Death from Mentally Murdered E.P.
- Son of Mr. Green Genes – Frank Zappa from Hot Rats
- Sweet Oddysee Of A Cancer Cell T’ Th’ Center Of Yer Heart – Mercury Rev from Yerself Is Steam
- Dead Embryonic Cells – Sepultura from Arise
- Cells – They Might Be Giants from Here Comes Science (songs for kids about science)
- White Blood Cells LP by The White Stripes
- Anything by The Membranes
- Soma – Smashing Pumpkins from Siamese Dream
- Golgi Apparatus – Phish from Junta
- Cell-scape LP by Melt Banana
Album covers with science images
Godflesh – Selfless. Scanning EM image of some cells growing on a microchip?
Circle – Meronia. Photograph of an ampuole?
Do you know any other science songs or album covers? Leave a comment!
Back of the envelope calculations for this post.
An old press release for a paper on endocytosis by Tom Kirchhausen contained this fascinating factoid:
The equivalent of the entire brain, or a football field of membrane, is turned over every hour
If this is true it is absolutely staggering. Let’s check it out.
A synaptic vesicle is ~40 nm in diameter. So the surface area of 1 vesicle is
which is 5026 nm2, or 5.026 x 10-15 m2.
Now, an American football field is 5350 m2 (including both endzones), this is the equivalent of 1.065 x 1018 synaptic vesicles.
It is estimated that the human cortex has 60 trillion synapses. This means that each synapse would need to internalise 17742 vesicles to retrieve the area of membrane equivalent to one football field.
The factoid says this takes one hour. This membrane load equates to each synapse turning over 296 vesicles in one minute, which is 4.93 vesicles per second.
Tonic activity of neurons differs throughout the brain and actually 5 Hz doesn’t sound too high (feel free to correct me on this). We’ve only considered cortical neurons, so the factoid seems pretty plausible!
For an actual football field, i.e. Association Football. The calculation is slightly more complicated. This is because there is no set size for football pitches. In England, the largest is apparently Manchester City (7598 m2) while the smallest actually belongs to the greatest football team in the world, Crewe Alexandra (5518 m2).
A brain would hoover up Man City’s ground in an hour if each synapse turned over 7 vesicles per second, while Gresty Road would only take 5 vesicles per second.
What is less clear from the factoid is whether a football field really equates to an “entire brain”. Bionumbers has no information on this. I think this part of the factoid may come from a different bit of data which is that clathrin-mediated endocytosis in non-neuronal cells can internalise the equivalent of the entire surface area of the cell in about an hour. I wonder whether this has been translated to neurons for the purposes of the quote. Either way, it is an amazing factoid that the brain can turnover this huge amount of membrane in such a short space of time.
So there you have it: quanta quantified on quantixed.
The post title is from “Insane In The Brain” by Cypress Hill from the album Black Sunday.
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:
- Is there a relationship between Twitter mentions and views of papers?
- What is the fraction of views that are PDF vs HTML?
- 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.
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.
This 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.
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.
The 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.
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.
A colleague once told me that they only review three papers per year and then refuse any further requests for reviewing. Her reasoning was as follows:
- I publish one paper a year (on average)
- This paper incurs three peer reviews
- Therefore, I owe “the system” three reviews.
It’s difficult to fault this logic. However, I think that as a senior scientist with a wealth of experience, the system would benefit greatly from more of her input. Actually, I don’t think she sticks rigorously to this and I know that she is an Academic Editor at a journal so, in fact she contributes much more to the system than she was letting on.
I thought of this recently when – in the space of one week – I got three peer review requests, which I accepted. I began to wonder about my own debit and credit in the peer review system. I only have reliable data from 2010.
Reviews incurred as an author are in gold (re-reviews are in pale gold), reviews completed as a peer are in purple (re-reviews are in pale purple). They are plotted cumulatively and the difference – or the balance – is shown by the markers. So, I have been in a constant state of owing the system reviews and I’m in no position to be turning down review requests.
In my defence, I was for two years Section Editor at BMC Cell Biology which means that I contributed more to the system that the plot shows. Another thing is reviews incurred/completed as a grant applicant/referee. I haven’t factored those in, but I think this would take the balance down further. I also comment on colleagues papers and grant applications.
Thinking back, I’ve only ever turned down a handful of peer review requests. Reasons being either that the work was too far outside my area of expertise or that I had a conflict of interest. I’ve never cited a balance of zero as a reason for not reviewing and this analysis shows that I’m not in this category.
In case any Editors are reading this… I’m happy to review work in my area, but please remember I currently have three papers to review!
The post title comes from a demo recording by The Posies that can be found on the At Least, At Last compilation on Not Lame Recordings.