Inside the PLOS ONE Academic Editor studio, part 1.

How did you end up being an Academic Editor at PLOS ONE?

Well, I was asked, then pondered on it for a day, and said yes. Quite simple, actually. And apparently, this is how it is usually done: another editor recommends you, you get an invitation from the journal office, submit your CV for perusal, and then you’re either in or out.

That’s all good, but why did you choose to become and editor?

I am an editor for two smaller societal journals, one aimed for amateur ornithologists and one on infection ecology and epidemiology, so I knew what was expected of me. However, the main reasons were academic solidarity and promotion open access publishing. That may sounds a bit presumptuous and aloft, but I think it is important to see science as something that is different from other lines of business. In my case, I have published >100 papers. If we assume that 1-6 reviewers have read each paper, depending on whether they were accepted in the first journal or passed on to other journals, this means several hundred peers have been evaluating my work. That is quite a work load, done by unpaid peers – and without that commitment science wouldn’t work. I have always tried to do as many referee assignments as possible, but now I am in a position to also contribute to the editor role more widely.

I heard the word ‘open access’ there, is that an important concept for you?

Yes, it is. Good science should be accessible for everyone, especially when based on taxpayers’ money. However, open access is not a religion, and I think it is important that we acknowledge that there are pros and cons with this way of publishing, including the balance on how much auxiliary data that need to go with a publication, for instance. In any case, the plus side is way larger than the down side, and I sincerely believe that open access journals are the future of scholarly publishing.

Why PLOS ONE, and not any of the other journals out there?

Well, PLOS ONE was first to ask, he he he. But flattery aside, I also have a very good publishing history with the journal, and its sister journals. My first paper in PLOS ONE was published in 2007, back when it was a very new journal. In fact, we hadn’t really figured out how the journal worked, and thought it was the most selected of the PLOS’ journals. That paper on avian malaria speciation was first submitted to Science, where it was out on review, but rejected in the second round. Anyway, it found a good home in PLOS ONE and has to date been viewed more than 7000 times and cited 44 times. I am pleased.

After that I have submitted many articles to PLOS ONE, sometimes as the first choice, sometimes after being turned down in general societal journals. My experience has been very positive, and we have nearly always got constructive critique from reviewers. What I really, really like is that the articles are accessible directly after publication, and that figures and other materials can be shared – for instance, on our blog. Collectively, this has made me very positive to the journal, and I am happy to now serve as an Academic Editor.

Thank you very much, Jonas. I think we need to stop here for a commercial break, but when we return I would like to ask you more of what you do as an editor, and what authors that submit articles should think about.

You’re welcome. I’d love to chat about that.

TO BE CONTINUED… (at a later date)


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To share, or not to share your data – some thoughts on the new data policy for the PLOS journals

By Jonas Waldenström

This post was intended as a comment on a post by Terry McGlynn at Small Pond Science, but once I started writing it soon swelled and I transformed into a blog post instead. I suggest you start with reading the original post here. The short version is: the leading scientific publisher PLOS have taking the open access movement one step ahead, from not only making the studies freely available, but to also make it mandatory to include the original data used to draw the conclusions of a paper. It seems as a good thing – too many studies can’t be replicated, much data is lost when people leave science, or are deposited in ways that don’t stand the test of time. However, it is also problematic, as good quality data is painstakingly hard to gather and could be viewed as a currency on its own.

I have published quite frequently with PLOS journals, and in particular with PLOS ONE. In fact, over the last couple of years I have authored/co-authored 15 papers in in PLOS ONE and two in PLOS Pathogens. My experiences so far have been very positive: good review processes, beautiful final prints, and, because of absence of pay walls, a very good spread among peers. I regularly check the altmetrics of the articles and it is exiting to see how many times they are viewed, downloaded, and cited. I have been very pro-PLOS, even in times when many ecologist peers didn’t consider PLOS ONE as a venue for publication. However, all the good things with PLOS considered, the new policy launched a little while ago have made me a bit more reluctant for submitting future work to the journal.

So what has changed? Is it a revolution, or ‘same old, same old’ – the answer is no one knows for sure (at least I don’t). The short version of the policy was published as an editorial in PLOS Biology, and although it states that the new data policy will make ‘more bang for the buck’ and ‘foster scientific progress’ it wasn’t overly clear what it means in practice for the researcher about to submit a paper:

 PLOS defines the “minimal dataset” to consist of the dataset used to reach the conclusions drawn in the manuscript with related metadata and methods, and any additional data required to replicate the reported study findings in their entirety. Core descriptive data, methods, and study results should be included within the main paper, regardless of data deposition. PLOS does not accept references to “data not shown”. Authors who have datasets too large for sharing via repositories or uploaded files should contact the relevant journal for advice.

In many cases it wouldn’t make a huge difference. There are already options to upload supporting data as appendixes, and repositories like Figshare, Genebank and Dryad are already out there. As an example, in one paper published in PLOS Pathogens we had 20 supplementary files, including details on statistical analyses, plenty of extra tables and figures. And for another publication in Molecular Ecology, the full alignments of genes analyzed were uploaded (per the journal instructions) to Dryad to facilitate others to replicate the analysis if need be. But for much of my current work on long-term pathogen dynamics in waterfowl it wouldn’t feel good to upload all the raw data. The question is really what a minimal dataset is. And importantly, what data you don’t include in the dataset.

A FAQ from PLOS has been published where this is addressed, but as of yet it remains to see how this is done in practice:

The policy applies to the dataset used to reach the conclusions drawn in the manuscript with related metadata and methods and any additional data required to replicate the reported study findings in their entirety. You need not submit your entire dataset, or all raw data collected during an investigation, but you must provide the portion that is relevant to the specific study.

So why am I a bit reluctant? Let me give you some background. The study system I run was started 12 years ago by professor Björn Olsen, and I have taken over the running of it 5-6 years ago. Over the years we have published quite many papers on avian influenza in this migratory Mallard population, but it is now when the time series is long enough that we can do more advanced studies on the effects of immunity on disease dynamics, long-term subtype dynamics, and influenza A virus evolution. Big stuff, based on the same large datasets but analyzed in different ways. If publishing one paper now means we have to submit 12 years of original data, i.e. the ringing data and disease data of 22,000 mallards, then it comes with a potential cost. I see the dataset as a work in progress, a living entity that is accumulating new data as we go along and where analyses are planned for both now-now and in the distant future. In the cow analogy of Terry McGlynn, the dataset is a herd with a balanced age structure, some cattle destined for the pot already today, some fattening for slaughter, and yet others to grow into the breeding stock.

The unique longevity of the time series has gotten our research into much fruitful collaboration. Since a few years we work with capture-mark-recapture researchers in France for making epidemiological models, just to name one aspect. I have also turned down invitations to collaborate, although much more rarely. In those instances it has been because we have planned to do these analyses ourselves, or that the time wasn’t right to do so. And just to make this clear: the cases of not sending data were not refusals to send background data for replicating a paper, rather they were requests to do new stuff with it. With posting your raw data in close to its entirety, such situations could be cumbersome, and you run the risk of seeing your data being analyzed by someone else.

The problem is much less on genetic data. After all, it is conventional in all fields to submit your sequences to Genebank along with your submission, and you know that it works. I have seen ‘our’ sequences in many phylogenetic trees without having been asked about the usage in advance. But it is one thing where your data is used as a brick in a new construction, and another to have someone taking over your house and having to give away the key. Many people say that risks of getting scooped of your data are exaggerated, and this is likely true. Scientists are usually decent people, after all. But, knowing there is such a risk, albeit small, can make an impact on publication options, or to delay publication of smaller papers until all the big papers from a dataset have been published (which may become problem for graduate student theses).

It is essential that we very soon gets to know what a minimal dataset is. For example, would it be OK to submit the raw data on Mallard infection histories without the unique ring number? Exchanging the individual identifier with an arbitrary number, for example. Or to exclude data such as actual date, morphology or indexes of condition? That way an analysis on the prevalence of a disease in a population (which is a very simple exercise) doesn’t immediately lead to the possibility for another researcher to investigate the effect of infection on a bird’s condition, effect of immunity, migration dynamics, to name a few options. Would PLOS allow that? I don’t know.

An issue raised by Terry McGlynn was the differences between a small lab and those at resource-intense research labs. In a small lab, the research takes longer time due to limited resources and smaller staff, and a good dataset is extremely precious and could also act as a currency, enabling co-authorships through collaborations. I would like to end with an additional story. A story of long-term data collected by volunteers at Ottenby Bird Observatory.

Ottenby Bird Observatory was founded in 1946 and have run without end since then. Each year volunteers help out with the trapping and banding of birds, mostly passerines, but also a chunk of waders, ducks and birds of prey. The number of birds caught each year is between 15 and 20 thousands, and in total more than 1 million birds have been banded. This dataset, together with all morphometrics collected in connection with the banding and all band recoveries is a unique and extremely valuable data series. The problem is that few pay for it. The observatory receives a small fund from the Swedish Environmental Board, but not enough to cover the costs. Additional money comes from tourists and subsidies from the Swedish Ornithological Society. And, although not substantial, from researchers that pay for the service of collecting data or getting data from the trapping series.

The observatory really provides a service. To date at least 278 peer-reviewed articles have been published with data emanating from Ottenby, including two papers in Science and >10 in PLOS journals. The unbroken trapping series has proven to be one of few datasets where the time scale is sufficiently long to investigate effects of climate change on biological phenologies, measured as timing of migration of common passerine birds. Researchers that want to use the data put in a request to the observatory, and a sort of contract is settled between the parties. Usually the money is little, but also small sums are essential for a volunteer-based operation. What happens when all data becomes immediately available for everyone without restrictions? A question to ponder, really.

In many ways, PLOS has revolutionized scholarly publishing and the open access movement has made research results available fast for the masses. I sincerely hope that the new data policy does not inadvertently work the opposite way, by making researchers less prone on submitting their studies to PLOS journals. It is still too early to tell, but I think many like me really wonders what the ‘minimal dataset’ really means in practice.


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Call of the wild: gulls as sentinels of antibiotic resistant bacteria

Gulls are our model species for antibiotic resistance dissemination. Picture from Wikimedia Commons.

Gulls are our model species for antibiotic resistance dissemination. Picture from Wikimedia Commons.

By Jonas Waldenström

What scare you the most, little one? Is it the monsters under your bed? Warfare, missiles and guns? Climate change? Spiders? Dogs with fangs? Yes, all of them are scary, and most are nasty. But if I have to choose among all the nastiness of this world I’d say antibiotic resistance scares me the most.

You have certainly heard about it, again and again. A researcher, or physician in a white lab coat saying that the situation is “worrisome” or even “alarming”. But it doesn’t really sink in. We don’t want to listen. We hurry, rush the kids to school and daycare, work dull hours in the office and dream about a better life. Or at least considers the substitutes for happiness:  a nicer car, a holiday in the sun, or a tight bum. But dreams sift like sand and we settle for a bottle of wine and a piece of beef on a Friday night. We are too occupied to ponder the grander questions, and leave the future for another day.

But if? Think about it.

What if antibiotics didn’t work? What kind of world would that be? We take these drugs for granted, but they are fairy stuff; twinkling wondrous inventions that can be gone in wink, rendered useless by evolution. You see, no one can escape evolution, and we have played the game wrong for as long as we have had antibiotics at our disposal. Instead of safeguarding, we have peppered the bacteria in animals, ourselves and the environment with antibiotics thereby increased the reward for resistance mutations to the point that they rapidly increase in frequency. In our hospitals and stables we have created environments were bacteria could mingle in antibiotic-cladded environments, promoting bacteria to share plasmid-borne resistance markers through horizontal transmission. Bad play, really bad play.

We talk misuse on an epic scale: an ever-accelerated prescription of drugs for any types of infections, even viral infections, where, of course, antibiotics do little good. We have used our drugs on food animals to treat infections, but also as growth promoters in chickens and swine; we have poured bucket loads of antibiotics in ponds to grow shrimps in clear-cut mangrove swamps; we have sprayed antibiotics on apple and fruits to keep them fresh until the retail level. But all this came with a cost, an interest rate we were not considering at the time. And soon it is time to settle the bill, but like Greece we have little means to pay.

When the magic bullets don’t hit their mark we will face a harsh reality. A post-antibiotic world. Science writer Maryn McKenna recently wrote an essay in Medium on how this brave new world would be. A world where simple infections could kill, a return to an era before the two world wars. No more knee implants and hip replacements, no more bowel surgery or nose jobs. A long kiss goodnight on our future health care.

But it can’t be that bad can it, you may ask yourself. They must exaggerate! Clearly, if things were so dire, the Government would do something about it! Well, truth is, we do far too little, governments included. We are standing with one foot leaning over the Pit of Doom (to use Fantasy jargon) and only a concerted action could take us out of it. Simply put, it is tragedy of the commons, where many small decisions end up in a big bad one.

To turn the tiller and set a new course we all need to chip in. Governments need to stimulate research in new drug developments. Global action needs to be taken for how to use antibiotics; these drugs are too potent to be sold over the counter without prescription. And antibiotics should not be used when they are not needed – and farm size and practices need to be addressed in the light of reducing consumption of antibiotics, not in the light of maximizing profit. And there need to be basic science. Our research group addresses the occurrence of resistance in the environment. Using gulls as model species we have travelled wide and far, from pole to pole, and sampled birds for antibiotic resistant bacteria. A few days ago we published our latest article on resistance dissemination in Europe. And it is a scary read.

Antibiotic resistance can attack you when you least expect it. Picture from Wikmedia Commons

Antibiotic resistance can attack you when you least expect it. Picture from Wikmedia Commons

A problem in most investigations, especially those conducted on wildlife, is that studies have been small – often a bunch of samples collected without proper sampling design or power calculation. Few studies have addressed larger spatial scales, beyond the country level. Graduate student Johan Stedt set out to change this, and his thesis – that he will defend in June – investigates the occurrence and frequency of resistance markers in gull Escherichia coli on a global scale. In the summer of 2009 (time flies fast in science between fieldwork and published articles) we sent out three teams of trained fieldworkers. In each car there was a liquid nitrogen dewar and sacks of sterile cotton wool swabs. Using our network of ornithologists across Europe, put in place by earlier flu virus studies, we were able to sample gull breeding colonies in nine European countries, from Spain and Portugal in the south, to Scandinavia in the north and the Baltic states in the east. All in all 3152 samples were collected during two weeks of fieldwork. This is by far the largest study conducted in wildlife across Europe.

In the lab, Johan spent months and months going through the samples. First, a primary isolation was done to get putative E. coli isolates. Then the identity of the bacterium needed to be validated with phenotypic test, and then the susceptibility of each isolate was tested with disc diffusion against a panel of 10 antibiotic agents. For the untrained ear they have strange, but beautiful names: ampicillin, cefadroxil, chloramphenicol, nalidixic acid, nitrofurantoin, mecillinam, tetracycline, tigecycline, streptomycin and trimethoprim/sulfamethoxazole. They are, however, commonly used in human and veterinary medicine.

But how is resistance quantified? It is actually rather simple. The isolate is inoculated onto an agar plate where little discs containing antibiotics are attached. The plate goes into a 37C heating cabinet for 24 hours and then one measure how close to the antibiotic discs the bacteria grow. A susceptible bacterium cannot grow close to the disc (where the antibiotic is leaked or ‘diffused’ into the agar), leaving a large zone devoid of growth called the inhibition zone. A resistant bug, on the other hand, can cope with the antibiotic compound and will therefore grow closer to the disc. Resistance is not always a black or white thing. Rather, there is a range of phenotypes with varying susceptibility to a compound. In clinical practice, breakpoints have been established for different bacteria and antibiotics by plotting the range of phenotypes for a large number of samples. This usually gives a normal distribution around the mean for susceptible bacteria, and a hump of isolates as outliers representing the resistant fraction.

Inhibition zones encircle the antibiotic discs in susceptible isolates. Picture from Wikimedia Commons

Inhibition zones encircle the antibiotic discs in susceptible isolates. Picture from Wikimedia Commons

Let’s return to the gulls. It pretty soon became apparent that resistant bacteria were common and widespread in European gulls. In fact, roughly a third of the isolates retrieved were resistant to at least one antibiotic compound, and a fair proportion was resistant to several. Looking at specific resistance profiles, the most frequently recovered phenotypes were resistant to tetracycline or ampicillin. These results are in concordance with other studies on gulls and may reflect the fact that these antibiotics have been commonly used in both veterinary and human medicine for decades. The occurrence of other resistance phenotypes, such as mecillinam and nalidixic acid resistance, was more surprising. Tigecycline was the only tested antibiotic that we did not find any resistance to; perhaps due to it being a relatively new antibiotic, used for skin-structure infections and complicated intra-abdominal infections.

A striking finding was the geographical variation in resistance levels. Have a look at the map above. Samples from the Iberian Peninsula were on average more often resistant than samples from gulls in more northern countries. This was true for all tested antibiotics (and also for ESBLs, but that is something that will be covered in another publication) and is also mirrored in similar data from humans and food animals in the EU. This south-to-north gradient can have many explanations, but likely reflects true differences in usage of antibiotic compounds across Europe.

Why gulls? What our research has indicated in this and other studies is that gulls are very convenient model species for dissemination of resistant bacteria in the environment. They are everywhere, especially where there is concentration of people, animals, and waste products. The twist in this study was to sample the birds during breeding times, when birds are most sedentary and where results therefore are more likely to depict the local situation.

Finally, what does this tell us? Should we worry about some resistant bugs in gull? The answer is yes, we should. You, me, we all should care – and we should act! Clearly there are no strict boundaries between the everyday lives of humans and the organisms that occur in the environment. The bugs we select for in our hospitals and in agriculture do not stay there – they leak, finding their way out into nature. The gull story shows that similar patterns occur across Europe, the situation in gulls is mirroring the situation in anthropogenic sources. They are our canaries for the antibiotic mines, our whistleblowers. And it is a vivid illustration of the magnitude of the resistance problem we are facing in the future.

Link to the paper:

Stedt, J., Bonnedahl, J., Hernandez, J., McMahon, B.J., Hasan, B., Olsen, B., Drobni, M. & Waldenström, J. 2014. Antibiotic resistance patterns in Escherichia coli from gulls in nine European countries. Infection Ecology and Epidemiology  4: 21565


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