Why you should ignore altmetrics and other bibliometric nightmares

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This discussion seemed to be of sufficient general interest that we submitted is as a feature to eLife, because this journal is one of the best steps into the future of scientific publishing. Sadly the features editor thought that " too much of the article is taken up with detailed criticisms of research papers from NEJM and Science that appeared in the altmetrics top 100 for 2013; while many of these criticisms seems valid, the Features section of eLife is not the venue where they should be published". That’s pretty typical of what most journals would say. It is that sort of attitude that stifles criticism, and that is part of the problem. We should be encouraging post-publication peer review, not suppressing it. Luckily, thanks to the web, we are now much less constrained by journal editors than we used to be.

Here it is.

Scientists don’t count: why you should ignore altmetrics and other bibliometric nightmares

David Colquhoun¹ and Andrew Plested²

¹ University College London, Gower Street, London WC1E 6BT

² Leibniz-Institut für Molekulare Pharmakologie (FMP) & Cluster of Excellence NeuroCure, Charité Universitätsmedizin,Timoféeff-Ressowsky-Haus, Robert-Rössle-Str. 10, 13125 Berlin Germany.

Jeffrey Beall is librarian at Auraria Library, University of Colorado Denver.  Although not a scientist himself, he, more than anyone, has done science a great service by listing the predatory journals that have sprung up in the wake of pressure for open access.  In August 2012 he published “Article-Level Metrics: An Ill-Conceived and Meretricious Idea.  At first reading that criticism seemed a bit strong.  On mature consideration, it understates the potential that bibliometrics, altmetrics especially, have to undermine both science and scientists.

Altmetrics is the latest buzzword in the vocabulary of bibliometricians.  It attempts to measure the “impact” of a piece of research by counting the number of times that it’s mentioned in tweets, Facebook pages, blogs, YouTube and news media.  That sounds childish, and it is. Twitter is an excellent tool for journalism. It’s good for debunking bad science, and for spreading links, but too brief for serious discussions.  It’s rarely useful for real science.

Surveys suggest that the great majority of scientists do not use twitter (7 — 13%).  Scientific works get tweeted about mostly because they have titles that contain buzzwords, not because they represent great science.

What and who is Altmetrics for?

The aims of altmetrics are ambiguous to the point of dishonesty; they depend on whether the salesperson is talking to a scientist or to a potential buyer of their wares.

At a meeting in London , an employee of altmetric.com said “we measure online attention surrounding journal articles” “we are not measuring quality …” “this whole altmetrics data service was born as a service for publishers”, “it doesn’t matter if you got 1000 tweets . . .all you need is one blog post that indicates that someone got some value from that paper”. 

These ideas sound fairly harmless, but in stark contrast, Jason Priem (an author of the altmetrics manifesto) said one advantage of altmetrics is that it’s fast “Speed: months or weeks, not years: faster evaluations for tenure/hiring”.  Although conceivably useful for disseminating preliminary results, such speed isn’t important for serious science (the kind that ought to be considered for tenure) which operates on the timescale of years. Priem also says “researchers must ask if altmetrics really reflect impact” .  Even he doesn’t know, yet altmetrics services are being sold to universities, before any evaluation of their usefulness has been done, and universities are buying them.  The idea that altmetrics scores could be used for hiring is nothing short of terrifying. 

The problem with bibliometrics

The mistake made by all bibliometricians is that they fail to consider the content of papers, because they have no desire to understand research. Bibliometrics are for people who aren’t prepared to take the time (or lack the mental capacity) to evaluate research by reading about it, or in the case of software or databases, by using them.   The use of surrogate outcomes in clinical trials is rightly condemned.  Bibliometrics are all about surrogate outcomes.

If instead we consider the work described in particular papers that most people agree to be important (or that everyone agrees to be bad), it’s immediately obvious that no publication metrics can measure quality.  There are some examples in How to get good science (Colquhoun, 2007).  It is shown there that at least one Nobel prize winner failed dismally to fulfil arbitrary biblometric productivity criteria of the sort imposed in some universities (another example is in Is Queen Mary University of London trying to commit scientific suicide?).

Schekman (2013) has said that science

“is disfigured by inappropriate incentives. The prevailing structures of personal reputation and career advancement mean the biggest rewards often follow the flashiest work, not the best.”

Bibliometrics reinforce those inappropriate incentives.  A few examples will show that altmetrics are one of the silliest metrics so far proposed.

The altmetrics top 100 for 2103

The superficiality of altmetrics is demonstrated beautifully by the list of the 100 papers with the highest altmetric scores in 2013    For a start, 58 of the 100 were behind paywalls, and so unlikely to have been read except (perhaps) by academics. 

The second most popular paper (with the enormous altmetric score of 2230) was published in the New England Journal of Medicine.  The title was Primary Prevention of Cardiovascular Disease with a Mediterranean Diet.  It was promoted (inaccurately) by the journal with the following tweet:

Many of the 2092 tweets related to this article simply gave the title, but inevitably the theme appealed to diet faddists, with plenty of tweets like the following:

The interpretations of the paper promoted by these tweets were mostly desperately inaccurate. Diet studies are anyway notoriously unreliable. As John Ioannidis has said

"Almost every single nutrient imaginable has peer reviewed publications associating it with almost any outcome."

This sad situation comes about partly because most of the data comes from non-randomised cohort studies that tell you nothing about causality, and also because the effects of diet on health seem to be quite small. 

The study in question was a randomized controlled trial, so it should be free of the problems of cohort studies.  But very few tweeters showed any sign of having read the paper.  When you read it you find that the story isn’t so simple.  Many of the problems are pointed out in the online comments that follow the paper. Post-publication peer review really can work, but you have to read the paper.  The conclusions are pretty conclusively demolished in the comments, such as:

“I’m surrounded by olive groves here in Australia and love the hand-pressed EVOO [extra virgin olive oil], which I can buy at a local produce market BUT this study shows that I won’t live a minute longer, and it won’t prevent a heart attack.”

We found no tweets that mentioned the finding from the paper that the diets had no detectable effect on myocardial infarction, death from cardiovascular causes, or death from any cause.  The only difference was in the number of people who had strokes, and that showed a very unimpressive P = 0.04. 

Neither did we see any tweets that mentioned the truly impressive list of conflicts of interest of the authors, which ran to an astonishing 419 words.

“Dr. Estruch reports serving on the board of and receiving lecture fees from the Research Foundation on Wine and Nutrition (FIVIN); serving on the boards of the Beer and Health Foundation and the European Foundation for Alcohol Research (ERAB); receiving lecture fees from Cerveceros de España and Sanofi-Aventis; and receiving grant support through his institution from Novartis. Dr. Ros reports serving on the board of and receiving travel support, as well as grant support through his institution, from the California Walnut Commission; serving on the board of the Flora Foundation (Unilever). . . “

And so on, for another 328 words. 

The interesting question is how such a paper came to be published in the hugely prestigious New England Journal of Medicine.  That it happened is yet another reason to distrust impact factors.  It seems to be another sign that glamour journals are more concerned with trendiness than quality.

One sign of that is the fact that the journal’s own tweet misrepresented the work. The irresponsible spin in this initial tweet from the journal started the ball rolling, and after this point, the content of the paper itself became irrelevant. The altmetrics score is utterly disconnected from the science reported in the paper: it more closely reflects wishful thinking and confirmation bias.

The fourth paper in the altmetrics top 100 is an equally instructive example.

But this paper was hidden behind Science’s paywall.  It’s bordering on irresponsible for journals to promote on social media papers that can’t be read freely.  It’s unlikely that anyone outside academia had read it, and therefore few of the tweeters had any idea of the actual content, or the way the research was done.  Nevertheless it got “1,479 tweets from 1,355 accounts with an upper bound of 1,110,974 combined followers”.  It had the huge Altmetrics score of 1848, the highest altmetric score in October 2013.

Within a couple of days, the story fell out of the news cycle.  It was not a bad paper, but neither was it a huge breakthrough.  It didn’t show that naturally-produced metabolites were cleared more quickly, just that injected substances were cleared faster when the mice were asleep or anaesthetised.  This finding might or might not have physiological consequences for mice.

Worse, the paper also claimed that “Administration of adrenergic antagonists induced an increase in CSF tracer influx, resulting in rates of CSF tracer influx that were more comparable with influx observed during sleep or anesthesia than in the awake state”.  Simply put, giving the sleeping mice a drug could reduce the clearance to wakeful levels.  But nobody seemed to notice the absurd concentrations of antagonists that were used in these experiments: “adrenergic receptor antagonists (prazosin, atipamezole, and propranolol, each 2 mM) were then slowly infused via the cisterna magna cannula for 15 min”.  Use of such high concentrations is asking for non-specific effects.  The binding constant (concentration to occupy half the receptors) for prazosin is less than 1 nM,  so infusing 2 mM is working at a million times greater than the concentration that should be effective. That’s asking for non-specific effects.  Most drugs at this sort of concentration have local anaesthetic effects, so perhaps it isn’t surprising that the effects resembled those of ketamine.

The altmetrics editor hadn’t noticed the problems and none of them featured in the online buzz.  That’s partly because to find it out you had to read the paper (the antagonist concentrations were hidden in the legend of Figure 4), and partly because you needed to know the binding constant for prazosin to see this warning sign.

The lesson, as usual, is that if you want to know about the quality of a paper, you have to read it. Commenting on a paper without knowing anything of its content is liable to make you look like an jackass.

A tale of two papers

Another approach that looks at individual papers is to compare some of one’s own papers.  Sadly, UCL shows altmetric scores on each of your own papers.  Mostly they are question marks, because nothing published before 2011 is scored.  But two recent papers make an interesting contrast.  One is from DC’s side interest in quackery, one was real science.  The former has an altmetric score of 169, the latter has an altmetric score of 2.

Compare this with the fate of some real, but rather technical, science.

Conclusions about bibliometrics

Bibliometricians spend much time correlating one surrogate outcome with another, from which they learn little.  What they don’t do is take the time to examine individual papers.  Doing that makes it obvious that most metrics, and especially altmetrics, are indeed an ill-conceived and meretricious idea. Universities should know better than to subscribe to them.

Although altmetrics may be the silliest bibliometric idea yet, much this criticism applies equally to all such metrics.  Even the most plausible metric, counting citations, is easily shown to be nonsense by simply considering individual papers.  All you have to do is choose some papers that are universally agreed to be good, and some that are bad, and see how metrics fail to distinguish between them.  This is something that bibliometricians fail to do (perhaps because they don’t know enough science to tell which is which).  Some examples are given by Colquhoun (2007) (more complete version at dcscience.net).

Eugene Garfield, who started the metrics mania with the journal impact factor (JIF), was clear that it was not suitable as a measure of the worth of individuals.  He has been ignored and the JIF has come to dominate the lives of researchers, despite decades of evidence of the harm it does (e.g.Seglen (1997) and Colquhoun (2003) )  In the wake of JIF, young, bright people have been encouraged to develop yet more spurious metrics (of which ‘altmetrics’ is the latest).  It doesn’t matter much whether these metrics are based on nonsense (like counting hashtags) or rely on counting links or comments on a journal website.  They won’t (and can’t) indicate what is important about a piece of research- its quality.

People say – I can’t be a polymath. Well, then don’t try to be. You don’t have to have an opinion on things that you don’t understand. The number of people who really do have to have an overview, of the kind that altmetrics might purport to give, those who have to make funding decisions about work that they are not intimately familiar with, is quite small.  Chances are, you are not one of them. We review plenty of papers and grants.  But it’s not credible to accept assignments outside of your field, and then rely on metrics to assess the quality of the scientific work or the proposal.

It’s perfectly reasonable to give credit for all forms of research outputs, not only papers.   That doesn’t need metrics. It’s nonsense to suggest that altmetrics are needed because research outputs are not already valued in grant and job applications.  If you write a grant for almost any agency, you can put your CV. If you have a non-publication based output, you can always include it. Metrics are not needed. If you write software, get the numbers of downloads. Software normally garners citations anyway if it’s of any use to the greater community.

When AP recently wrote a criticism of Heather Piwowar’s altmetrics note in Nature, one correspondent wrote: "I haven’t read the piece [by HP] but I’m sure you are mischaracterising it". This attitude summarizes the too-long-didn’t-read (TLDR) culture that is increasingly becoming accepted amongst scientists, and which the comparisons above show is a central component of altmetrics.

Altmetrics are numbers generated by people who don’t understand research, for people who don’t understand research. People who read papers and understand research just don’t need them and should shun them.

But all bibliometrics give cause for concern, beyond their lack of utility. They do active harm to science.  They encourage “gaming” (a euphemism for cheating).  They encourage short-term eye-catching research of questionable quality and reproducibility. They encourage guest authorships: that is, they encourage people to claim credit for work which isn’t theirs.  At worst, they encourage fraud. 

No doubt metrics have played some part in the crisis of irreproducibility that has engulfed some fields, particularly experimental psychology, genomics and cancer research.  Underpowered studies with a high false-positive rate may get you promoted, but tend to mislead both other scientists and the public (who in general pay for the work). The waste of public money that must result from following up badly done work that can’t be reproduced but that was published for the sake of “getting something out” has not been quantified, but must be considered to the detriment of bibliometrics, and sadly overcomes any advantages from rapid dissemination.  Yet universities continue to pay publishers to provide these measures, which do nothing but harm.  And the general public has noticed.

It’s now eight years since the New York Times brought to the attention of the public that some scientists engage in puffery, cheating and even fraud.

Overblown press releases written by journals, with connivance of university PR wonks and with the connivance of the authors, sometimes go viral on social media (and so score well on altmetrics).  Yet another example, from Journal of the American Medical Association involved an overblown press release from the Journal about a trial that allegedly showed a benefit of high doses of Vitamin E for Alzheimer’s disease.

This sort of puffery harms patients and harms science itself.

We can’t go on like this.

What should be done?

Post publication peer review is now happening, in comments on published papers and through sites like PubPeer, where it is already clear that anonymous peer review can work really well. New journals like eLife have open comments after each paper, though authors do not seem to have yet got into the habit of using them constructively. They will.

It’s very obvious that too many papers are being published, and that anything, however bad, can be published in a journal that claims to be peer reviewed .  To a large extent this is just another example of the harm done to science by metrics  –the publish or perish culture. 

Attempts to regulate science by setting “productivity targets” is doomed to do as much harm to science as it has in the National Health Service in the UK.    This has been known to economists for a long time, under the name of Goodhart’s law.

Here are some ideas about how we could restore the confidence of both scientists and of the public in the integrity of published work.

• Nature, Science, and other vanity journals should become news magazines only. Their glamour value distorts science and encourages dishonesty.
• Print journals are overpriced and outdated. They are no longer needed.  Publishing on the web is cheap, and it allows open access and post-publication peer review.  Every paper should be followed by an open comments section, with anonymity allowed.  The old publishers should go the same way as the handloom weavers. Their time has passed.
• Web publication allows proper explanation of methods, without the page, word and figure limits that distort papers in vanity journals.  This would also make it very easy to publish negative work, thus reducing publication bias, a major problem (not least for clinical trials)
• Publish or perish has proved counterproductive. It seems just as likely that better science will result without any performance management at all. All that’s needed is peer review of grant applications.
• Providing more small grants rather than fewer big ones should help to reduce the pressure to publish which distorts the literature. The ‘celebrity scientist’, running a huge group funded by giant grants has not worked well. It’s led to poor mentoring, and, at worst, fraud.  Of course huge groups sometimes produce good work, but too often at the price of exploitation of junior scientists
• There is a good case for limiting the number of original papers that an individual can publish per year, and/or total funding. Fewer but more complete and considered papers would benefit everyone, and counteract the flood of literature that has led to superficiality.
• Everyone should read, learn and inwardly digest Peter Lawrence’s The Mismeasurement of Science.

A focus on speed and brevity (cited as major advantages of altmetrics) will help no-one in the end. And a focus on creating and curating new metrics will simply skew science in yet another unsatisfactory way, and rob scientists of the time they need to do their real job: generate new knowledge.

It has been said

“Creation is sloppy; discovery is messy; exploration is dangerous. What’s a manager to do?
The answer in general is to encourage curiosity and accept failure. Lots of failure.”

And, one might add, forget metrics. All of them.

Follow-up

17 Jan 2014

This piece was noticed by the Economist. Their ‘Writing worth reading‘ section said

"Why you should ignore altmetrics (David Colquhoun) Altmetrics attempt to rank scientific papers by their popularity on social media. David Colquohoun [sic] argues that they are “for people who aren’t prepared to take the time (or lack the mental capacity) to evaluate research by reading about it.”"

20 January 2014.

Jason Priem, of ImpactStory, has responded to this article on his own blog. In Altmetrics: A Bibliographic Nightmare? he seems to back off a lot from his earlier claim (cited above) that altmetrics are useful for making decisions about hiring or tenure. Our response is on his blog.

20 January 2014.

Jason Priem, of ImpactStory, has responded to this article on his own blog, In Altmetrics: A bibliographic Nightmare? he seems to back off a lot from his earlier claim (cited above) that altmetrics are useful for making decisions about hiring or tenure. Our response is on his blog.

23 January 2014

The Scholarly Kitchen blog carried another paean to metrics, A vigorous discussion followed. The general line that I’ve followed in this discussion, and those mentioned below, is that bibliometricians won’t qualify as scientists until they test their methods, i.e. show that they predict something useful. In order to do that, they’ll have to consider individual papers (as we do above). At present, articles by bibliometricians consist largely of hubris, with little emphasis on the potential to cause corruption. They remind me of articles by homeopaths: their aim is to sell a product (sometimes for cash, but mainly to promote the authors’ usefulness).

It’s noticeable that all of the pro-metrics articles cited here have been written by bibliometricians. None have been written by scientists.

28 January 2014.

Dalmeet Singh Chawla,a bibliometrician from Imperial College London, wrote a blog on the topic. (Imperial, at least in its Medicine department, is notorious for abuse of metrics.)

29 January 2014 Arran Frood wrote a sensible article about the metrics row in Euroscientist.

2 February 2014 Paul Groth (a co-author of the Altmetrics Manifesto) posted more hubristic stuff about altmetrics on Slideshare. A vigorous discussion followed.

5 May 2014. Another vigorous discussion on ImpactStory blog, this time with Stacy Konkiel. She’s another non-scientist trying to tell scientists what to do. The evidence that she produced for the usefulness of altmetrics seemed pathetic to me.

7 May 2014 A much-shortened version of this post appeared in the British Medical Journal (BMJ blogs)