Meditation on a Caltrain: Understanding where to travel to next
Exploring options and thinking laterally about where you can use your scientific skills might be the key to successfully transitioning into industry, learns George Busby.
This piece was one of two winners of the Science Innovation Union writing competition, Oxford.
“This is downtown San Francisco, our train’s final stop. Can all passengers please detrain? All detrain please. All detrain.” Perhaps it was the heady fug of jetlag that made this broadcast particularly amusing to my UK-English language sensibilities, but I “detrained” all the same and stepped into the crisp morning air of the Californian rush hour.
I was on the west coast to visit two genetics start-ups as part of a whirlwind three-day tour of the US. With a long postdoc and several first author papers tucked into my belt, I wanted to see if these credentials would pass muster in the tech haven of Silicon Valley. I’ve always found the loneliness of solo work-travel to be highly amenable to strategic thought, and this American adventure was an opportunity to reflect on why I was there and what I wanted.
Back in Oxford, a few months earlier, I had begun to line-up my post-postdoc career options. A new and exciting big-data research institute has just opened and my supervisors were keen that I apply for money to start my own research group there. Excited by the prospect of doing interesting science somewhere new, I began to piece together the semblance of a research proposal with collaborative support.
But then a strange thing happened. As the project began to take shape, the light at the end of the tunnel — the prize of scientific independence — began to feel not closer, but further away. Ahead of me were late nights and early mornings of writing pages and pages of a scientific proposal. After that, a year-long wait to find out that I’d been unsuccessful (a mere 15-20% of applicants for an early career Wellcome Trust Sir Henry Dale Fellowship get funded). Despite everything, my future was dependent on a number of factors that were out of my control.
On top of this, there was the burgeoning realisation that no one actually reads the academic papers that I write. This is no moot point: writing papers is the main purview of a research scientist, and the central way we both communicate our results and measure success. However, compared to the proportion of the world’s population who can read, the number of people that had sat down to ingest my latest, dense, and fascinating (to me at least) treaty on the population genetics of Africa, three years in the making, was minuscule. The words of a colleague rang in my head: “99.9% of scientific papers just don’t get read”.
Did I really want to spend the next 18 months slogging it out against funding agencies to get my own money just to do yet more science that no one was going to read? I forced myself to think more fundamentally about what I wanted to do. If I wanted to use my science to make a real and lasting impact and do things that make a real difference in the world, then writing academic papers is only one route to success.
So, I blew the cobwebs off my LinkedIn account and started to hit up my small network of commercial contacts to investigate what companies out there in the big wide world might value my hard-won scientific expertise. This led me to California, where the streets are paved with gold and to the heart of the world’s tech industry.
I’m by no means the first, and will certainly not be the last, person to have grown tired of the uncertainty of pursuing an academic research career. Despite the best efforts of university career departments, the option of staying in academia has always felt like the only real way to keep doing the science that I wanted to do: any other path would force a compromise or feel like I was quitting. But, perhaps I’d been looking at things the wrong way round. Rather than proposing whatever research was ‘hot’ at any given moment to funding bodies to maintain a decent university career trajectory, I should instead consider what my scientific ambitions are, then find the place to do them without limiting myself to academia.
This way of thinking — that I could achieve my scientific objectives without compromise in either academia or industry — has been made possible for two reasons. Firstly, by luck as much as design. I work in a field, human genomics, where there are increasing options for work outside of universities: the number of commercial enterprises is exploding. If there was ever a time to jump into industry, it’s now. Second, I’d underappreciated how employable I am. I’ve led methodological and analytical research projects, written papers, and worked to communicate my science. Coupled with some in-depth genomics knowledge, these are all highly desirable qualities in the biotech world.
So I reached out to two Californian companies, both of which do scientific research that’s not a million miles away from my day-to-day. Visiting them allowed me to see with my own eyes how work in industry differed from academia. I was surprised to learn that research jobs at both companies were not purely about making marketable products: there was a certain amount of trial and error to the work that they do, and not all of the research that they do is expected to end up as a viable product. They were also both mature enough to have teams of people working on marketing, accounts, PR, and software engineers, who were supported by the sales of the main product, but not scientists themselves. The possibility of collaborating with these people is exciting, providing new avenues for communicating and justifying the work of the research teams.
Importantly, both companies sell my flavour of science to millions of customers — working for them would mean I could impact orders of magnitude more people, orders of magnitude more quickly than any scientific research I could hope to do in a university over the next few years. If impact and scientific reach is what I want, then this seems like a far better way to achieve it than waiting for a year to hear on the unlikely success of a research grant.
I was beginning to feel like Lady Justice with my balance scales measuring the benefits and costs of academic versus commercial employment. Sure, academic research is dominated by uncertain funding cycles and can feel glacially slow at times, but that’s not necessarily a bad thing. Some view it as a privilege to be able to devote one’s time exclusively to fully understanding a specific question, and there’s no denying the satisfaction that comes with finding stuff out. Plus, I’ve been fortunate enough to work with incredibly talented people who’ve given me the intellectual freedom to spend my days thinking about the things that I want to think about. There’s clearly a lot to be said for being able to concentrate on the questions that one believes to be important and worthwhile.
But with a wife and a growing family I’ve also reached the age where the pursuit of such scholarly freedom might appear not just selfish, but irresponsible. In common with around a third of UK families, both my wife and I work full time. Without my wife’s additional income, my postdoc salary would give us a higher household income than around 42% of the population. So, despite almost ten years at university (and the debt to prove it) without two incomes, we’d be struggling to get above the median of household earners nationwide. And the double whammy of living in the least affordable city in the UK with the cost of childcare increasing at three times inflation year on year, even with two incomes, there is little monthly return on my educational investment. Moreover, from a purely financial point of view, it pays to work in industry as a life scientist, with salaries being up to 30% higher than academia.
As peers from school and university began to financially pull away from me, first by buying cars that are younger than ten years old, and more recently upgrading their small flats for family houses, I’ve consoled myself in the knowledge that although I can’t match them, I’m doing what I love. Who needs things anyway? But when you’re spending a third of your take-home pay on rent and another third on childcare, there’s little chance of saving much of the remaining third. Realising that you’re never going to be able to buy a house in the city where you work starts to get mentally draining. Can I really justify doing the science I do, which, let’s remember, no one actually reads, to just about get by? Of course, I’m far from being a pauper, or even a JAM, but wouldn’t it be nice for either my wife or myself to reduce the hours we work to spend more time with our children, without having to drastically change our quality of life?
There is of course risk of job security associated with working in industry, particularly for an early stage start-up. But, there is also risk associated with staying in academia, particularly given the number of PhD and postdoc scientists in the workforce, many of whom will be pushing for the same jobs. And, in industry there is the distinct possibility that your pay could match your scientific success, which is not the case when you’re tied to a public sector pay scale.
More than anything, my visit to California not only demonstrated that it’s possible to do interesting and worthwhile science commercially, but that perhaps it’s the only way to do some science. It would take many years and much grant money to generate the sorts of big datasets that some tech companies now have control of. If, as a scientist, you’re interested in answering some of the big questions, perhaps it pays to ask yourself whether the best way to achieve your ambitions is through a start-up, rather than academically. What’s more, at least in genomics, it’s beginning to feel like detraining from the academic express onto the industry platform might be the best way to do the most relevant and engaging science.
George Busby is a postdoctoral research associate in statistical genomics at the Wellcome Trust Centre for Human Genetics, University of Oxford.
Is Science In Trouble?
A Conversation With Colin Camerer About the Replication Crisis News Writer: Emily Velasco Credit: Caltech If there's a central tenet that unites all of the sciences, it's probably that scientists should approach discovery without bias and with a healthy dose of skepticism. The idea is that the best way to reach the truth is to allow the facts to lead where they will, even if it's not where you intended to go. But that can be easier said than done. Humans have unconscious biases that are hard to shake, and most people don't like to be wrong. In the past several years, scientists have discovered troubling evidence that those biases may be affecting the integrity of the research process in many fields. The evidence also suggests that even when scientists operate with the best intentions, serious errors are more common than expected because even subtle differences in the way an experimental procedure is conducted can throw off the findings. When biases and errors leak into research, other scientists attempting the same experiment may find that they can't replicate the findings of the original researcher. This has given the broader issue its name: the replication crisis. Colin Camerer Colin Camerer, Caltech's Robert Kirby Professor of Behavioral Economics and the T&C Chen Center for Social and Decision Neuroscience Leadership Chair, executive officer for the Social Sciences and director of the T&C Chen Center for Social and Decision Neuroscience, has been at the forefront of research into the replication crisis. He has penned a number of studies on the topic and is an ardent advocate for reform. We talked with Camerer about how bad the problem is and what can be done to correct it; and the "open science" movement, which encourages the sharing of data, information, and materials among researchers.What exactly is the replication crisis? What instigated all of this is the discovery that many findings—originally in medicine but later in areas of psychology, in economics, and probably in every field—just don't replicate or reproduce as well as we would hope. By reproduce, I mean taking data someone collected for a study and doing the same analysis just to see if you get the same results. People can get substantial differences, for example, if they use newer statistics than were available to the original researchers. The earliest studies into reproducibility also found that sometimes it's hard to even get people to share their data in a timely and clear way. There was a norm that data sharing is sort of a bonus, but isn't absolutely a necessary part of the job of being a scientist.How big of a problem is this? I would say it's big enough to be very concerning. I'll give an example from social psychology, which has been one of the most problematic areas. In social psychology, there's an idea called priming, which means if I make you think about one thing subconsciously, those thoughts may activate related associations and change your behavior in some surprising way. Many studies on priming were done by John Bargh, who is a well-known psychologist at Yale. Bargh and his colleagues got young people to think about being old and then had them sit at a table and do a test. But the test was just a filler, because the researchers weren't interested in the results of the test. They were interested in how thinking about being old affected the behavior of the young people. When the young people were done with the filler test, the research team timed how long it took them to get up from the table and walk to an elevator. They found that the people who were primed to think about being old walked slower than the control group that had not received that priming. They were trying to get a dramatic result showing that mental associations about old people affect physical behavior. The problem was that when others tried to replicate the study, the original findings didn't replicate very well. In one replication, something even worse happened. Some of the assistants in that experiment were told the priming would make the young subjects walk slower, and others were told the priming would make them walk more quickly—this is what we call a reactance or boomerang effect. And what the assistants were told to expect influenced their measurements of how fast the subjects walked, even though they were timing with stopwatches. The assistants' stopwatch measures were biased compared to an automated timer. I mention this example because it's the kind of study we think of as too cute to be true. When the failure to replicate came out, there was a big uproar about how much skill an experimenter needs to do a proper replication.You recently explored this issue in a pair of papers. What did you find? In our first paper, we looked at experimental economics, which is something that was pioneered here at Caltech. We took 18 papers from multiple institutions that were published in two of the leading economics journals. These are the papers you would hope would replicate the best. What we found was that 14 out of 18 replicated fairly well, but four of them didn't. It's important to note that in two of those four cases, we made slight deviations in how the experiment was done. That's a reminder that small changes can make a big difference in replication. For example, if you're studying political psychology and partisanship and you replicate a paper from 2010, the results today might be very different because the political climate has changed. It's not that the authors of the original paper made a mistake, it's that the phenomenon in their study changed. In our second paper, we looked at social science papers published between 2010 and 2015 in Science and Nature, which are the flagship general science journals. We were interested in them because these were highly cited papers and were seen as very influential. We picked out the ones that wouldn't be overly laborious to replicate, and we ended up with 21 papers. What we found was that only about 60 percent replicated, and the ones that didn't replicate tended to focus on things like priming, which I mentioned before. Priming has turned out to be the least replicable phenomenon. It's a shame because the underlying concept—that thinking about one thing elevates associations to related things—is undoubtedly true.How does something like that happen? One cause of findings not replicating is what we call "p-hacking." P-value is a measure of the statistical likelihood that your hypothesis is true. If the p-value is low, an effect is highly unlikely to be a fluke due to chance. In social science and medicine, for example, you are usually testing whether changing the conditions of the experiment changes behavior. You really want to get a low p-value because it means that the condition you changed did have an effect. P-hacking is when you keep trying different analyses with your data until you get the p-value to be low. A good example of p-hacking is deleting data points that don't fit your hypothesis—outliers—from your data set. There are statistical methods to deal with outliers, but sometimes people expect to see a correlation and don't find much of one, for example. So then they think of a plausible reason to discard a few outlier points, because by doing that they can get the correlation to be bigger. That practice can be abused, but at the same time, there sometimes are outliers that should be discarded. For example, if subjects blink too much when you are trying to measure visual perception, it is reasonable to edit out the blinks or not use some subjects. Another explanation is that sometimes scientists are simply helped along by luck. When someone else tries to replicate that original experiment but doesn't get the same good luck, they won't get the same results.In the sciences, you're supposed be impartial and say, "Here's my hypothesis, and I'm going to prove it right or wrong." So, why do people tweak the results to get an answer they want? At the top of the pyramid is outright fraud and, happily, that's pretty rare. Typically, if you do a postmortem or a confessional in the case of fraud, you find a scientist who feels tremendous pressure. Sometimes it's personal—"I just wanted to be respected"—and sometimes it's grant money or being too ashamed to come clean. In the fraudulent cases, scientists get away with a small amount of deception, and they get very dug in because they're really betting their careers on it. The finding they faked might be what gets them invited to conferences and gets them lots of funding. Then it's too embarrassing to stop and confess what they've been doing all along.There are also faulty scientific practices less egregious than outright fraud, right? Sure. It is the scientist who thinks, "I know I'm right, and even though these data didn't prove it, I'm sure I could run a lot more experiments and prove it. So I'm just going to help the process along by creating the best version of the data." It's like cosmetic surgery for data. And again, there are incentives driving this. Often in Big Science and Big Medicine, you're supporting a lot of people on your grant. If something really goes wrong with your big theory or your pathbreaking method, those people get laid off and their careers are harmed. Another force that contributes to weak replicability is that, in science, we rely to a very large extent on norms of honor and the idea that people care about the process and want to get to the truth. There's a tremendous amount of trust involved. If I get a paper to review from a leading journal, I'm not necessarily thinking like a police detective about whether it's fabricated. A lot of the frauds were only uncovered because there was a pattern across many different papers. One paper was too good to be true, and the next one was too good to be true, and so on. Nobody's good enough to get 10 too-good-to-be-trues in a row. So, often, it's kind of a fluke. Somebody slips or a person notices and then asks for the data and digs a little further.What best practices should scientists follow to avoid falling into these traps? There are many things we can do—I call it the reproducibility upgrade. One is preregistration, which means before you collect your data, you publicly explain and post online exactly what data you're going to collect, why you picked your sample size, and exactly what analysis you are going to run. Then if you do very different analysis and get a good result, people can question why you departed from what you preregistered and whether the unplanned analyses were p-hacked. The more general rubric is called open science, in which you act like basically everything you do should be available to other people except for certain things like patient privacy. That includes original data, code, instructions, and experimental materials like video recordings—everything. Meta-analysis is another method I think we're going to see more and more of. That's where you combine the results of studies that are all trying to measure the same general effect. You can use that information to find evidence of things like publication bias, which is a sort of groupthink. For example, there's strong experimental evidence that giving people smaller plates causes them to eat less. So maybe you're studying small and large plates, and you don't find any effect on portion size. You might think to yourself, "I probably made a mistake. I'm not going to try to publish that." Or you might say, "Wow! That's really interesting. I didn't get a small-plate effect. I'm going to send it to a journal." And the editors or referees say, "You probably made a mistake. We're not going to publish it." Those are publication biases. They can be caused by scientists withholding results or by journals not publishing them because they get an unconventional result. If a group of scientists comes to believe something is true and the contrary evidence gets ignored or swept under the rug, that means a lot of people are trying to come to some collective conclusion about something that's not true. The big damage is that it's a colossal waste of time, and it can harm public perceptions of how solid science is in general.Are people receptive to the changes you suggest? I would say 90 percent of people have been very supportive. One piece of very good news is the Open Science Framework has been supported by the Laura and John Arnold Foundation, which is a big private foundation, and by other donors. The private foundations are in a unique position to spend a lot of money on things like this. Our first grant to do replications in experimental economics came when I met the program officer from the Alfred P. Sloan Foundation. I told him we were piloting a big project replicating economics experiments. He got excited, and it was figuratively like he took a bag of cash out of his briefcase right there. My collaborators in Sweden and Austria later got a particularly big grant for $1.5 million to work on replication. Now that there's some momentum, funding agencies have been reasonably generous, which is great. Another thing that's been interesting is that while journals are not keen on publishing a replication of one paper, they really like what we've done, which is a batch of replications. A few months into working on the first replication paper in experimental economics funded by Sloan, I got an email from an editor at Science who said, "I heard you're working on this replication thing. Have you thought about where to publish it?" That's a wink-wink, coy way of saying "Please send it to us" without any promise being made. They did eventually publish it.What challenges do you see going forward? I think the main challenge is determining where the responsibility lies. Until about 2000, the conventional wisdom was, "Nobody will pay for your replication and nobody will publish your replication. And if it doesn't come out right, you'll just make an enemy. Don't bother to replicate." Students were often told not to do replication because it would be bad for their careers. I think that's false, but it is true that nobody is going to win a big prize for replicating somebody else's work. The best career path in science comes from showing that you can do something original, important, and creative. Replication is exactly the opposite. It is important for somebody to do it, but it's not creative. It's something that most scientists want someone else to do. What is needed are institutions to generate steady, ongoing replications, rather than relying on scientists who are trying to be creative and make breakthroughs to do it. It could be a few centers that are just dedicated to replicating. They could pick every fifth paper published in a given journal, replicate it, and post their results online. It would be like auditing, or a kind of Consumer Reports for science. I think some institutions like that will emerge. Or perhaps granting agencies, like the National Institutes of Health or the National Science Foundation, should be responsible for building in safeguards. They could have an audit process that sets aside grant money to do a replication and check your work. For me this is like a hobby. Now I hope that some other group of careful people who are very passionate and smart will take up the baton and start to do replications very routinely.
Uncertain Airspace: Changing career paths is disorienting and exhilarating
Pursuing a new career makes PhD student Jonathan Wosen feel like a baby goose—and he loves it. Sometimes I ask people, “if you weren’t studying biology, what would you do?” At first, they’re taken aback, and I don’t blame them. PhD students are self-selected for a certain kind of persistent, focused thinking; that’s what it takes to become the world’s leading expert on your thesis project. We are as deeply immersed in our work as a fish in water. That makes asking a graduate student to consider a different field of study a lot like asking a fish to imagine life on dry land. Initially, there’s some flailing of fins and gasping for air, but the answers come. “I think I would do computer science, or engineering.” “Maybe chemistry, or biochemistry. Is that too close to biology to count?” “It would be fun to try math.” In my experience, the responses are all variations on a single theme: most students would opt for some other STEM field. But my answer doesn’t fit the mold. I would go into journalism. From an early age, I was awed by newscasters’ power to shape my perception of the world. With a single report, they could expose corruption, challenge governments, and make me care about people and places I had never heard of. These experiences left me with a deep interest in how news stories are told as well as what and whose stories are told. Nevertheless, science remained my primary passion. Ever since elementary school, when I told my principal that I wanted to be a lab technician, I’ve never considered another career. That’s pretty odd given that there are no scientists in my family, who emigrated from Ethiopia in the 80s before taking up low-wage jobs in east San Diego. I chalk it up to all the hours spent watching Bill Nye the Science Guy and The Magic School Buswhen I wasn’t watching the news. The logic behind applying to graduate school was simple: I wanted to be a scientist, scientists have PhDs, and therefore I should get one. If only what followed had been so straightforward. Progress on my project, which involves growing finicky stem cells to learn about celiac disease, has been excruciatingly slow. I love learning about science and sharing my knowledge with others, but the day-to-day minutiae of my research project does wear me down. Last year, during my third year of graduate school, I was constantly anxious and stressed, and, worst of all, didn’t tell anyone that I was struggling. I felt obligated to stick to the script I had written for myself: the boy who dreamt of becoming a scientist and never stopped until he reached his goal. My family and friends had bought into this narrative too, and I didn’t want to disappoint them. Plus, deep down, I still hoped to become a professor and help diversify academia; it was difficult to think that there would be one fewer black faculty member. At first, I was ambivalent about pursuing a career in science communication, and kept telling myself that I should focus on research. I was interested enough to take a course, though, and there I found a community of students, professors, and professionals who cared as much about public outreach as I did. Part of the reason I first got interested in biomedical research was because of the public benefits of studying health and disease. I realized that empowering people with an understanding of major scientific discoveries was another form of public service. After feeling siloed within my own project, it was refreshing to hear journalists talk about reading up on a wide range of scientific discoveries and having the freedom to ask basic questions. Throughout the course, I could feel my natural excitement and scientific curiosity start to return. I checked out books from the library on science writing, contacted editors for freelancing opportunities, and shared my aspirations with friends and family. So far, their responses have all been positive. So that’s where I’m at right now. I think that finishing my PhD will open new and better opportunities, so that’s the plan. In the meanwhile, I intend to get as much communications experience as I can—blogging, podcasting, and writing for publications in the coming years. In eastern Greenland (trust me, this is relevant), barnacle geese nest in towering, rocky cliffs that keep young goslings away from predators but also away from food. Eventually, the goslings must leave their nests for the green fields below. There’s only one problem: they can’t fly. What they do instead is literally jump off the cliff, spreading their tiny, fluffy bodies to create drag and desperately try to steer themselves towards a (relatively) soft landing. It’s a wonder that any of them survive. In a sense, I feel like one of those goslings right now. Suspended in uncertain airspace, embracing the unknown, steering myself towards a better future. Oh, and hoping that I don’t crash on the way down. Jonathan Wosen is an immunology PhD student at Stanford. You can expect more writing from this young gosling as he learns to navigate the world of science communication.
Start-ups: A sense of enterprise
Universities aid entrepreneurs by helping them to turn their research into companies. In return, universities can reap financial benefits. Michael Schrader knew he wanted to create a company, but he wasn't sure what it should do. After six years as a mechanical engineer in the automotive industry building plastic parts, in 2010 he began a master's degree in business administration at Harvard Business School in Boston, Massachusetts. In his quest for inspiration, he took a course in commercializing science at the Harvard Innovation Lab (i-lab). The class heard presentations from researchers who among them had developed 17 different technologies that they thought had commercial value. One in particular caught Schrader's attention — a method devised by two engineers from Tufts University that uses a silk protein to stabilize vaccines. The vaccines could be formulated as powders and mixed with water when it was time to inject them, or embedded into a film that dissolves on the tongue like a breath-freshening strip. And, because they would not need to be refrigerated, they would be easier than conventional vaccines to distribute in places such as sub-Saharan Africa. Along with other members of his class — an economics master's student, a former physics student earning a law degree and a postdoc in the chemistry department — Schrader spent the next few months looking into potential markets for the technology, making connections with business mentors and investors, and putting together a business plan. In 2012, the team founded Vaxess Technologies, which is attempting to bring vaccine formulations to market. “We probably are a perfect model for how universities can forge together entrepreneurs and technologies to create companies,” says Schrader, now chief executive of Vaxess. The technology has not yet entered clinical testing, but the company has raised more than US$5 million, hired 11 employees, and started filing patents of its own in addition to those it licensed from Tufts University. Although universities often license technology developed in their research laboratories to existing companies that are looking for new products, they also move discoveries off the bench and into the real world by encouraging inventors to start businesses from scratch. They offer classes in entrepreneurship, introduce researchers to investors and business experts, and even launch their own venture-capital funds. The path is trickier for life-sciences spin-offs, which take more time and money to get off the ground, than for companies based on software or electronics. And Europe has not caught up with the United States in its ability to create businesses. But universities are banking on entrepreneurs turning some of their research into products (see 'Start-up sampler'). Table 1: Start-up sampler Universities seeking to commercialize research spin off scores of companies. These examples show the range of entrepreneurship spawned in the life sciences. Full size table Hubs of innovation “We exist on taxpayer money. We have an obligation to try to get our research out into society.” Universities tend to see commercialization as part of their remit to create and disseminate knowledge. “We exist on taxpayer money. We have an obligation to try to get our research out into society,” says Regis Kelly, director of the California Institute for Quantitative Biosciences known as QB3. The institute is a collaboration between the Berkeley, Santa Cruz and San Francisco campuses of the University of California. It supports life-sciences research across the campuses and tries to bring that research to market by partnering with industry and promoting entrepreneurship. Part of the mission of the University of Colorado Boulder's BioFrontiers Institute is to aid students and faculty members who want to start new companies, says Jana Watson-Capps, associate director of the institute. “It fits with what we want to do in providing an education for our students so that they can find jobs and be good at those jobs,” she says. A similar attitude is common in the United Kingdom. “We think it's important here in Oxford to see that the fruits of our research are actually developed to benefit society,” says Linda Naylor, managing director of Isis Innovation, a company created by the University of Oxford to commercialize its research. Harvard's i-lab, which was opened in late 2011 to help students in any of the university's schools to develop businesses, is a relatively new entry in a long line of such efforts at many academic institutions. Students learn about idea generation, business-plan development and marketing. Budding entrepreneurs can attend workshops on specific hurdles that they are likely to encounter, such as how to apply for a Small Business Innovation Research grant from the federal government. A group of 'experts in residence' provides students with business expertise and introduces them to potential investors. The i-lab holds competitions such as the President's Challenge, which awards ideas that address the world's big problems. Vaxess took the challenge's top prize of $70,000 in 2012, as well as winning $25,000 in Harvard's Business Plan Contest the same year. Because the main thrust of the i-lab is education, the university never takes a stake in any of the companies created there, says managing director Jodi Goldstein. Any intellectual property developed in a Harvard research lab belongs to the university and must be licensed, but ideas generated in the i-lab belong to the students. Goldstein hopes that the i-lab can help a future Mark Zuckerberg or Bill Gates to pursue their billion-dollar idea while still completing their degree. “We have several pretty famous dropouts around here, and I don't think that's necessary anymore,” she says. As well as education and expertise, the i-lab provides a workspace for fledging companies. Meeting rooms, computer workstations and private storage space are available, as are a workshop for building prototypes and a pair of 3D printers. The i-lab is also planning to address one of the stumbling blocks that often trips up biology-based companies: finding a space to turn a discovery made in a university lab into a more marketable version. It is building a 1,400-square-metre wet lab with 36 research benches. When Vaxess reached that stage, it moved to LabCentral in Cambridge, Massachusetts. The provider of office and laboratory space takes care of regulatory requirements and provides administrative support and laboratory personnel so that new companies don't have to spend time and money setting up their own space. It opened in 2013 with a $5-million grant from the Massachusetts government (part of an initiative to bolster life-sciences business in the state) along with support from the Massachusetts Institute of Technology and the venture-capital arm of health-care giant Johnson & Johnson. Schrader considers this industry–government–academia web of support essential to his company's launch. “We have really taken advantage of this growing entrepreneurial ecosystem,” he says. At QB3 in California, start-ups can rent lab space for as little as $85–100 per square metre per month. Unlike conventional landlords, who prefer to rent out an entire space, start-ups can rent a few hours in a fume cupboard or a shelf in a freezer, for example. “You only pay for what you actually use,” Kelly says. Charging is important, mainly because it is a way of weaning its users off the university teat. “It gets people more used to being in the private sector,” he says. The need for lab space is just one reason why starting a life-sciences company can be much more challenging than, say, launching a business based on software. Any sort of pharmaceutical or medical device is subject to regulatory requirements, which leads to safety tests and clinical trials “If you're going to make a new drug you might need ten years and a billion dollars,” says Watson-Capps. These time and capital requirements make it much more difficult to drum up investment for a life-sciences start-up. Although investors might be willing to risk a couple of hundred thousand dollars on a promising software idea, most life-sciences companies need initial funding of a few million dollars. “Obviously, people don't want to throw away a million dollars, so they have to do a lot more due diligence,” Kelly says. And because the time to realize a return on the investment can be so long, trading equity in the company in exchange for, say, legal services is not as popular as it is for other types of start-ups, he adds. These disparities are apparent in the investment statistics. Of the $77.3 billion in venture capital invested in the United States in 2015, software companies took in $31.2 billion — 40% of the total. Pharmaceuticals and biotechnology received a mere 12%. Playing catch up Europe lags behind the United States in producing start-ups of any kind, but the situation is improving. “We're certainly seeing a lot more spin-outs than we were a few years ago,” says Naylor. “There is more money around that is willing to go into the early stage.” Vaxess Technologies are using silk proteins (L), which are extracted from cocoons (R), to stabilize vaccines. Image: Patrick Ho/Vaxess She attributes that growth, in part, to the UK government's creation of the Seed Enterprise Investment Scheme in 2012, which provides tax breaks to investors in start-up companies. “The UK has been one of the leaders in providing tax incentives for investors in start-ups of all types,” says Karen Wilson, who studies entrepreneurship and innovation at Bruegel, an economic think tank in Brussels. Other countries across Europe, as well as Australia, have created their own tax incentives for investors modelled on the British scheme, although Wilson says that they're often controversial, derided as tax breaks for the wealthy. In the United States, tax incentives vary by state. The biggest legal change in the United States to promote spin-offs came in 1980, Wilson says, with the passage of the Bayh–Dole act, which allowed researchers to profit from inventions created with federal funding. US and UK Universities have even been creating their own venture funds in recent years to invest in their spin-offs. The University of Cambridge, UK, created Cambridge Innovation Capital in 2013 with an initial fund of £50 million ($71 million). In 2014, the University of California began a $250-million fund. In May 2015, Isis launched Oxford Sciences Innovation to raise an initial £300 million from investors. And, in January, University College London opened the £50 million UCL Technology Fund, and the University of Bristol, UK, started its own enterprise fund (see 'Innovation income'). Box 1: Licensing technology: Innovation income When it comes to commercializing research, universities often emphasize their desire to spread their discoveries, but they also reap financial rewards from licensing technology and investing in spin-off companies. Isis Innovation, for instance, took in £24.6 million (US$34.9 million) in revenue in 2015, of which it returned £13.6 million to its founder Oxford University, UK, more than double 2014's £6.7 million. The university also earned more than £30 million in cash and stocks from the 2014 sale of the games and technology company NaturalMotion (in which it had a stake of about 9%) to Zynga in San Francisco, California, for $527 million. NaturalMotion was co-founded in 2001 by Torstein Reil, then a PhD student in Oxford's zoology department studying neural systems. Reil used his research to create computer simulations that more accurately mimic how animals move, and turned them into a company that makes popular games such as Clumsy Ninja. But licensing income tends to make up only a small part of a university's revenue stream. Harvard University in Cambridge, Massachusetts, which last year issued 50 licenses to patents it owns and saw 14 firms started on the basis of its technology, had licensing revenue of $16.1 million in 2015. But that is a fraction of Harvard's 2015 budget of nearly $4.5 billion, of which the university spent $876 million on research. Jana Watson-Capps, associate director of the University of Colorado Boulder's BioFrontiers Institute, says that income from all licensing — not just from spin-off companies — is valuable to the university and goes back into funding research. However, she adds, licensing income is relatively small and comes so long after the initial investment that it's not a major consideration at the institute. A similar attitude prevails at Oxford. Although the university welcomes the licensing income, it's not the only motive for promoting spin-offs, says Linda Naylor, managing director of Isis Innovation. “The university is very clear it wants to create impact,” she says. “They're not there to make any quick money.” Show more Entrepreneurial ecosystems in which inventors can find facilities, investors and business experts to help them to launch their companies are important for creating successful spin-offs, and they've been growing around many European universities, Wilson says. “There are an increasing number of these entrepreneurial hubs that are emerging across Europe, which are spawning these innovative high-growth firms,” she says. In the United Kingdom, Cambridge is popular for life-sciences start-ups, and in Munich, Germany, the focus is mobile technology. In Switzerland, start-ups are clustered around the University of Zurich and the Swiss Federal Institute of Technology in Lausanne, where they focus on computing and technology. In Finland, Espoo is a hub: in 2010, three institutions combined to form Aalto University, which has strengths in communications, energy and design. Linked by a bridge across the Øresund strait, Copenhagen and Malmo in Sweden, make up another life-sciences centre. In the past year, however, the influx of refugees from the Middle East has led to a tightening of border security and made crossing the bridge more difficult for everyone. The clampdown on migration within Europe, says Wilson, is making it harder for fledging companies to grow and spread. Expansion of their markets has always been challenging for start-ups in Europe, she says, where pushing into another country means dealing with differences not only in language and culture but also in taxes and other regulations. Many European companies get to a point at which, when they need to grow into a bigger market, they move to the United States, either of their own accord or at the insistence of their investors. “If you have a successful start-up in Italy it's much easier to go scale it in the US than it is to try to scale it across Europe,” Wilson says. But many life-sciences companies won't grow on their own, particularly if their innovation is a drug — their endgame is often to be acquired by a large pharmaceutical company once they have advanced their therapy to a promising stage. Although life-sciences companies demand more resources than other types of start-up, they have one characteristic that can make them uniquely appealing to investors — the potential for curing a disease or improving human health. As Kelly points out, “Almost any rich person has a sick relative.” If investors are going to risk their money, knowing that many of the companies they invest in will fail, they may prefer investments that have a potential for making a difference, he says. “If they're going to lose money on a business, they might as well lose it on something that could have some benefit to society.” Search our job roles in California