No scientist is too junior to fix the system

This first appeared in Nature: May 2017

The March for Science filled the streets on a Saturday afternoon. The next steps should come at research institutions and universities, says John Tregoning

                Last month thousands of researchers took to the streets. It is time to channel this collective energy to shape the culture of science.

                We all love to complain how the system for doing science thwarts ideal practice. Prestigious publications are rewarded more than sound work. Everyone ends up chasing trends and asking the same questions. Broader multidisciplinary research might achieve more, but it is harder to publish and less well rewarded. We end up sticking to the path of the prestigious paper and big grant at the expense of worthier endeavors.

                Why don’t we just change the system to something better? After all, science is uniquely self-regulating. The people who set the science agenda are scientists, the people who allocate funding are scientists, and the people who decide what gets published are scientists. The tool we hold in highest regard is peer-review: we are judge, jury and executioner.

One reason for stasis is that scientists value consistency. The scientific process requires controlling variables as tightly as possible, even down to those unlikely to have any impact on an experiment. I know people who won’t change the order in which they use pipette tips; they are unlikely to change the research system.

Another reason is that we’re too busy just getting by in the current system to pause to fix its flaws. Grant submissions and experimental timepoints—tasks that reward the individual and have strict deadlines--will always win against some nebulous effort for the common good.

But most of all there’s the sad reality that those who most feel the need for change have the least power to create it. It’s all too easy to justify putting off activism. The time to fix the system, we tell ourselves, is after we have gained actual influence. If a PhD student shouts in frustration, are things going to change, or will she just be marginalized as a rabble rouser?

This leads to a pernicious inertia: moving up the ladder shifts your perspective. Making tenure puts you in a position to make change, but can inure you to the status quo. The principal investigator tells the postdoc that finding a permanent position is nothing compared with the angst of getting a grant. The postdoc tells the PhD student that defending a thesis is nothing compared with the angst of finding a permanent position. The higher you rise, the smaller the problems of those in the levels below seem. In other words, research traps young scientists in a suboptimal system, but if they plan to advance their careers before setting it right, nothing will change.

Within the last twelve months, separate groups of researchers have made headlines [http://www.nature.com/news/the-mathematics-of-science-s-broken-reward-system-1.20987] by applying evolutionary fitness metaphors to show that scientists are driven to less rigorous but more ‘productive’ practices. They portray science as a zero-sum game: everyone is so busy competing that no one revises the rules. Those who spend their time lobbying for change rather than collecting data will find themselves scooped of the recognition required for resources.

But evolutionary theory also suggests a potential way out: reciprocal altruism. The key is to use whatever influence you do have to help your peers, and to trust that your peers will do the same. I have reaped the benefits. One example was relinquishing a key authorship position on a paper in order to maintain a productive collaboration. At the time, I felt I was losing out, not fighting hard enough in the struggle for the scarce resource of credit to which I felt justified. But the small sacrifice paid off. I continued to work with my co-authors, and we wrote a successful grant together. The immediate reward of prime authorship would have been less beneficial in the long run.

More broadly, I am collecting a group of like-minded colleagues that consciously try to be less self-focused and support each other. In practice, this comes down to small things that even a pipetting-compulsive can handle: we read each other’s drafts, accept a fair share of committee posts so no one has an undue burden, take the time to forward relevant grant announcements, or just to go out for a drink. We just each try to work a bit more toward a collective good: I happen to be enthusiastic about identifying broken stuff in the building that everyone else ignores (burnt out lights, squeaky doors, blocked sinks) and seeing that they get repaired.

Start now. Don’t wait on your senior colleagues, and definitely don’t wait until you become the senior colleague. Build a network of like-minded people. Identify something that doesn’t work and fix it. It can be as small as leaky tap or as big as peer review. Believe that idealism can be catching.

Reciprocal altruism may seem idealistic, but focusing solely on your own advancement can come back to bite you. Academic promotions and appointments to senior positions require recommendations from colleagues, and I’m sure I’m not the only one who has heard of ambitious individuals who would never be considered for department chair because they have stabbed too many people in the back.

Let’s strive to stand together. Historians called last month’s worldwide march to defend science unprecedented in terms of its scale and breadth. That energy and optimism need not dissipate – it should be funneled into making the overall system function better. The payoff may not occur immediately, but play the long game and we all can win.

How to turn 19,000 data points into 1 graph.

Science is stories.

Good stories move science forwards. The stories come from the data and turning data into a story is a long and iterative process. The more data you have the longer it can take, as our tools get better at producing more data per sample it is getting harder to find the story. In our recently published study (Inflammatory Responses to Influenza Vaccination at the Extremes of Age) we were measuring 27 different mediators after giving 2 different vaccines 3 times to 3 different ages of mouse, sampling at 8 timepoints after vaccination with 5 replicate animals at each timepoint leading to 19,440 data points. This was a tricky knot to unpick.

Inflammatory responses

The aim of the study was to investigate whether age changed the immune response to vaccination. In particular we were interested in whether age affected inflammation after immunisation. Inflammation sounds bad, but we actually need a small amount to kick the immune system and make the vaccine work. We know that vaccines work less well at the extremes of age and wanted to determine whether the initial reaction to the vaccine shaped how well it worked. To investigate the inflammatory response, we used a tool called Luminex. Luminex measures chemical messengers in the blood called cytokines; these chemical messengers recruit cells of the immune system to the site of vaccination, activate them and shape the type of response they generate. However, as mentioned, Luminex generates LOTS of data: 19,440 data points. The first time we had the complete dataset, we had to book a study room to have sufficient space to spread out all the bits of paper with the data on. So how did we move it from there into a story?

Data Compression

 It took four things –perseverance, perspective, peer review and bio-informatics.

Perseverance: With any dataset, but large ones in particular, time is the most critical factor in finding the story. You need to spend time with the dataset, getting to know it, formatting and reformatting: sorting by size, time, alphabetically, into classes of cytokines. Analysis can’t be done piecemeal; several times I would get close to understanding the data but then have to take time off to do something else and when I came back to the data would have forgotten the trends I had been close to identifying and have to start from scratch. There were several dead ends and times when I wanted to give up as there was no discernible pattern in the data.

Perspective: That said, analysis can’t all be done in one sitting. You need time for the subconscious to churn it through, you need to read around the subject to see what other people have seen, you need conversations with colleagues and chance insights when on the loo. The creative process can’t be rushed.

Peer-review: Exposing your precious story to the slings and arrows of outrageous review is often frustrating and can be soul-destroying. However, in this case (and I grudgingly admit quite frequently for other studies) peer review significantly improved the paper. It gave us time and perspective to rethink the conclusions and suggested new ways of analysing and thinking about the dataset.

Bioinformatics: It turns out that, whilst easy and accessible, excel may not be the most effective tool for looking at big datasets. There are a range of other bioinformatic tools, which can help in the analysis. In this case we used principal component analysis. Now I have no idea how the maths behind this actually works, but I do know it squishes the 19,000 or so variables into 2 so that you can then see broad trends in the data and then from there go back and look for individual variables of interest.

So what did we learn?                        

Having spent time staring at the data, a number of patterns did emerge. First of all, age is a major factor in the inflammatory response to vaccination; with different cytokines being produced in young, adult and elderly animals. Secondly adjuvants can shape the response. Adjuvants are compounds that improve vaccine efficacy; the addition of an adjuvant called MF59 reduced age associated differences, inducing higher levels of the cytokines IL-5, G-CSF, KC, and MCP-1. The level of these four cytokines correlated with the level of antibody produced after vaccination. This is important because it shows that poor responses at the extremes of age can be overcome through the addition of adjuvants; it also gives us some insight into what response to a vaccine can lead to the best results. Taking a complex (and large) dataset and turning it into a story was a lengthy process, but has helped us understand more about the immune response to vaccines.

From Great sweetness came forth infection.

Bacteria, like all living things, need food to grow. The bacteria that infect us are no exception to this and their food source is us! The airways are surprisingly rich in nutrients for bacterial growth, some of this comes from the food we ate (micro-inhalation) and some leaks out from the blood or cells lining the airways. We know that underlying lung diseases increase the risk of bacterial infection and have recently shown that this is related to the levels of glucose in the airways. We think that this works a little like leaving a jam jar open – bacteria will colonise and grow on the available sugar.

New Treatments for Bad Bugs

Antibiotic resistance bacteria (bacteria that are not killed by antibiotics) are a crisis in global health. If antibiotics stop working, as well as an increase in the severity infections that are treatable, much of the medical advances of the last 50 years including surgery and transplant also become ineffective. We therefore need new ways of killing bacteria. This could either be by finding drugs that directly attack the bacteria, or by changing strategies.

War on bugs

Our finding that bacteria grow better when sugar is high opens up new treatment strategies – to starve the bug, rather than attacking it. In our recent study, we investigated whether an anti-diabetic drug (Dapagliflozin, made by AstraZeneca) could prevent bacterial lung infection. Treating diabetic mice with Dapagliflozin reduced the blood sugar; critically it also reduced the airway sugar levels. The reduction in airway sugar led to a reduction in bacterial infection in the drug treated mice. We have seen a similar effect using another anti-diabetic drug – metformin. These studies suggest that reducing blood and lung sugar will reduce the number of infections seen in people with diabetes.

New Year's Resolution 2017

My first resolution is a work-centric one. It is not dissimilar to the resolution I made in 2016 (and 2015, 2014 and 2013). It is to publish 10 papers in the same year and to get promoted! In some ways, this is the academic equivalent of saying that I will quit smoking and lose 2st (12kg) in weight: it is aspirational, but lacks the detail needed to achieve it.

The second resolution is a political call to arms, to myself and the whole academic community. I think it is fair to say that we, the experts, lost 2016. Somewhere in post-truth politics, our voices stopped being heard. In the next four years, the truths I hold to be self-evident – that vaccines work, evolution happens and the climate is changing – will be under attack and no amount of clever Facebook posts that I make to my like-minded friends will help defend them. I need to come up with better ways to get the message across: fighting rhetoric with reason, fear with facts and populism with pragmatism.

It’s going to be a long year.

This post first appeared on Times Higher Education on the 5th Jan 2017

Support basic science

The promised injection of £2 billion into the UK science ecosystem is without doubt a good thing. However, there is some uncertainty as to how it will be handed out.

Since this is taxpayers’ money, there needs to be a demonstration that the money has been “well spent”: the big question, then, is what defines well-spent science funding? In the event of the government not opting for the “give it all to John Tregoning” option, I wanted to make a case for the funding of basic science.

Translation versus inspiration

While all science involves repeated testing of ideas, we artificially split the world of scientific effort into two very broad areas: basic science (pure research, learning about stuff for the sake of learning); and translational science (testing things like drugs, chemicals, devices, bridges and computers to improve the quality of human existence).

To those with a commercial mindset, the translational approach has the greater value. You put money in, you get better stuff out. So why invest in pure research?

Essentially, basic science underpins translational research: the ideas about how to make stuff better come out of pure research. Lots of modern engineering depends on us understanding how gravity works, but Newton’s aim wasn’t to put rockets on the moon. While the results are not immediately tangible, basic science underpins technologies that are the foundations of billion-dollar industries – for example cancer immunotherapy, lasers, the internet, GPS, fluorescent and luminescent proteins.

I strongly believe that we need both: funding translational science at the expense of basic science may pay off in the short term, but it damages advances in the long term.

The home of basic research

I also believe that in the current research ecosystem, universities are best placed to deliver the pure research and companies small and large are best placed to develop it into real things.

Companies utilise (and often contribute to) the basic research being performed by academia, but rarely initiate basic research programmes by themselves: though there are exceptions, the IBM Zurich Research Laboratory (which has gained two Nobels) has just celebrated its 60th year and the AT&T Bell labs earned 8 Nobel prizes.

If universities are initiating the research, it raises a question about who financially benefits from the basic research, as the money may not seem to come directly back to the originator. But it will trickle back in tax revenue, employment, better medicines, cleaner cars and other indirect benefits.

This is a strength of bringing Innovate UK and RCUK (Research Councils UK) into one umbrella organisation, enabling the flow from academic basic science to small and medium enterprise led innovation (ie, by any firm with up to 250 employees) to large company implementation.

Teaching

The other benefit of basic research is the teaching and training element.

The economy needs people with science backgrounds. A PhD provides the student with very much more than just the ability to move colourless liquids around – it gives them problem-solving, teamwork and analytical skills, tenacity, flexibility and independence. But just as no one expects doctors to train without ever seeing a patient, the best way to learn science is by doing science.

Basic research delivers this apprenticeship in science. To quote the National Science Foundation in the US: “Basic science is a gamble because it deals with the unknown, but a sure thing because it always leads to improvements in knowledge.”

Reap what you sow

The good news is that the public have repeatedly demonstrated support for basic science: a 2014 survey by the British Science Association reported that 8 out of 10 people questioned supported research with no immediate benefit. So please include basic research in the mix – not to the exclusion of work with an immediate pay-off, but as part of a long term strategy to further develop our scientific excellence.

To paraphrase John F. Kennedy: we choose to do the research we do, not because it is easy, but because it is hard; we choose to do basic science because it is there and new hopes for knowledge are there and we are going to climb these mountains.

Surely that is as uplifting a message as we can hope to end 2016 on.

This article was first published on the Times Higher Education Supplement 31/12/16

I need space to breathe, to create

Creativity – probably the best PI skill in the world

What is the most important skill to become a PI? An eye for numbers, an ability to perform repetitive tasks accurately, optimism in the face of relentless failure, the ability to play nicely with others, sheer bloody mindedness, self-belief? All of these skills will strap you into the driving seat but once there, you’ll need to press the pedals yourselves. The most vital skill is creativity; the ability to see new connections — linking old data in new ways and using what we do know to interpret what we don’t.

Creativity is the most nebulous, ephemeral, and elusive of qualities and often feels at odds to the scientific process, but without creativity, you ain’t going nowhere.

In my experience there’s an arc to developing an idea. It starts with staring in despair at a steaming pile of mismatched data that has recently been deposited onto your desk. After the initial shock, you might begin to see strands of a story coalescing. You start to sew it together, ambitiously demanding new datasets and proposing experiments that will never be undertaken.

Finally, you pull all of the ideas into a shining gem of scientific writing, polished to perfection for your dream journal, only to have it crushed by some faceless, nameless, and possibly soulless reviewer and have to begin again. However, these steps are extremely tricky and involve a lot of tea, pacing round the office and crumpled sheets of paper. Here are some things that may help you to have, and then develop your ideas.

Be receptive

Ideas come at the most inconvenient of times — at 4 in the morning or when you have no access to pen/paper/internet. Accept this and provide yourself with tools to mitigate it: keep a pen besides your bed; use the notes feature on your phone; carry a notebook everywhere.

Stand on the shoulders of giants: read

There are no new ideas. Everything is a development from something else: this makes it both easier and harder. Easier because you can read around and adapt ideas from other disciplines; harder because someone else has no doubt had the same idea, reducing its novelty, impact and therefore marketability.

Follow your dreams

Allow yourself periods of not actively thinking about an idea — when you come back to it the problem will often be clearer. A lot of the heavy lifting can be done by your subconscious; give it time to do the groundwork and feed it by reading around the topic. But try to keep it focussed, as it is prone to drift off to the land of chocolate (mmmm. Chocolate).

Work the problem

“My subconscious is working on my grant” is a great excuse, but doesn’t get you funded — you do actually have to do something. Even if all you have to show for it is a bin full of crumpled paper; sitting, thinking and writing are all needed to add substance to any idea. I’ve spent many mornings going round in circles stuck on a particular issue, but you need to put in those miles in order to achieve breakthroughs. The trickiest part is knowing when to push and when to stop.

Take a (mind) dump

Even short breaks can help. Archimedes had his Eureka moment in the bath, Newton was chillin’ by a tree when he got beaned by the apple good, and programmers have been communing with rubber ducks for 17 years. The first two probably didn’t happen (and the third, bizarrely, does). And there are other small rooms where water displacement and inspiration are linked  — stepping away from your desk can often lead to moments of clarity.

Don’t overthink it

Ideas are strange ephemeral things and in their earliest stages, they are staggeringly easy to destroy: direct scrutiny is the death of creativity. To paraphrase Douglas Adams, the brain just edits out them out, like a blind spot: your only hope is to catch them by surprise out of the corner of your eye. There is a difference between coming up with an idea, when you need to be creative, imaginative and think of the big picture; and developing an idea, when you need to be critical, analytical and focussed on the details.

It’s good to talk (but only sometimes)

It can help to discuss your idea with someone else as advice is always valuable, but you need to find the right person. Some people are good at giving unstructured support. Others are more critical, which can make your ideas stronger, but it can also kill them stone dead. Be clear with what you need when approaching someone for advice.

The timing of the discussion is critical. At their inception, when I can’t even find the words to describe the ideas to myself, there is no point trying to describe them to others; I get tongue-tied and frustrated while the person I am talking to just stares, bewildered. As the ideas become more formed, my excitement increases, but they are no less fragile.

When they’re developing, but not complete, the ideas (and I) both need unconditional praise to develop further: detailed questioning can make me doubt my idea, lose enthusiasm and bin the whole thing, including the good bits. Finally, only when fully mature, do I feel robust enough for ‘instant feedback’

The single best thing about academia is that you get to have ideas and test them, no matter how crazy they are. But you must feed the beast: it takes more than one good idea to sustain a career. Yes, a “break-in” idea might get you your first PI job, but maintain a stream of ideas at various stages of development from half-baked plan devised in the pub to rejected grant. So get out there and start thinking.

This article first appeared on Nature Jobs Blog on 19 Dec 2016.

Take my advice (or don't)

Academia is a complex, challenging, highly competitive career and it is easy to feel lost. In the absence of a simple route from PhD to professor, we are forced to hunt for advice.

The problem is how does one get good advice? There is certainly no shortage of it; advice can be found everywhere, from your mate in a pub beer garden; to colleagues, coaches, mentors and heads of department; through training courses, conferences and lectures; to books and the infinite echo chamber of the internet. Some of it is excellent (may I, ahem, recommend this excellent blog), some of it is excrement.

The problem is not really finding advice, but in acting upon the right advice. It can be because the advice is poor, but more often it is because the recipient is not receptive because of hubris, egocentric bias, emotional investment, mistiming, lack of head space, failure to understand or advice saturation.

Here are five scenarios in which advice, however good, may not be acted upon:

Unique and beautiful snowflakes: all of us face different challenges at different times. These challenges are different to those faced by the generation before us (the people we often turn to for advice). The circumstances and career path for me as a lecturer are different to the professors in front of me and the postdocs behind me, leading to a misalignment of advice and problem.

Change sucks: sometimes to act on advice requires change. Change is hard at the best of times; change when it implies you have been doing something wrong, impossible.

You just don’t understand me: incorporation of feedback is inversely proportional to emotional investment. Often the advice sought concerns a piece of work into which you have invested considerable effort, sweat and tears. It is easy to confuse feedback with criticism.

Ostrich approach: additionally, if the advice received about a paper or grant identifies a problem that is difficult to solve, it can be easier not to address it and hope that the reviewers don’t spot the same problem (trust me, they always do).

I just want to be loved: however, sometimes when we say we are looking for feedback, we are actually looking for validation. Honest feedback may be useful in the long run, but when you have hit a wall, there are times when encouragement and support are more valuable.

So how to get more out of advice received?

Respect your elders. The first place most of us look for advice is senior faculty, and there are two good reasons to listen to them. First, academia hasn’t changed that much since monks set up the first universities, so their experience is still relevant. Second, senior faculty sit on the grant panels and promotion boards that you are targeting. They know what works and more importantly what doesn’t work. If they raise red flags about your work, it is likely that their peers, who are evaluating you for real, will raise the same red flags. Don’t ignore feedback identifying problems in your work, however difficult they are to fix.

Role model(s). We all need a role model – someone who has got to where you want to be, in whose footsteps we can tread. Of these people, there will be some people with whom you resonate more, whose advice is phrased in a way that is easier for you to take. Identify them and turn to them more often.

But don’t stop at one – have many role models. The routes to the end are many and varied. Different people have different skills and experience that you can draw upon. Jim Collins, who teaches and writes about leadership, advises establishing your own personal board of directors. I use Peter for politics, Robin for Research, Alan for all matters recruitment, Charlie for choice words of support and Sarah for sense and sensibility (admittedly, I am lucky to have friends whose names conveniently align with their expertise).

They don’t all have to be university-based: people outside academia have useful opinions too.

Negative role model. While there are people with whom you resonate, there are inevitably others with whom you don’t, be it a bad ex-boss, an uncollaborative collaborator or a conniving colleague. Identify patterns of behaviour in these people whom you find loathsome and make an effort to do the opposite.

Be clear what you need. Advice can be great and there is no shortage of advice or people willing to give it. Don’t be shy about approaching people; everyone likes to give advice. But be clear in your mind when you need overly honest feedback and when you need a hug. Compartmentalise advisers into those who will give you the unpalatable truth and those who rose-tint your world. And when you do approach someone, be very specific with the questions you ask; if you say “what should I do with my life?” a professor doesn’t know where to begin. If you say “I am considering x or y but not sure how to think about it. I’d love you thoughts”, then it’s easier to engage and be practical.

Stress-test it. Finally, we are scientists, we test hypotheses. Take this approach to advice. The best way to decide whether to follow someone’s advice, is to see if it actually works. However, don’t let the adviser know or they may not be so forthcoming.

This article first appeared on the Times Higher Education website on 10th October 2016