Bench Press

A recent NBER paper (gated) by Benjamin Jones from Northwestern conducts a systematic review of trends in scientific research and made a couple of conclusions that won’t come as a surprise to anyone in science (HT: Inside Higher Ed):

As science advances and knowledge accumulates, ensuing generations of innovators spend longer in training and become more narrowly expert, shifting key innovations (i) later in the life cycle and (ii) from solo researchers toward teams

As evidence to this, the average age at which a scientist made a discovery which later qualified for a Nobel prize has increased by 6 years over the course of the 20th century. When looking at publications, the researchers found that the average author list on a publication grew, on average, by 15-20% per decade!

We’ve discussed before the “decline of the Lone Ranger model of science”, but Jones’ paper focuses on looking at the policy implications for such a change. He concludes that the government (and, probably, the academic and private institutions which support researchers) need to adapt policy to reflect this new reality by:

• Tailoring funding and messaging to help keep young researchers interested despite the longer and more difficult training period
• Finding new ways to evaluate the worthiness of proposals as scientist’s expertise becomes more and more specialized
• Altering incentive structures as the team of collaborators replaces the Lone Ranger scientist model of discovery

These policy suggestions are definitely good ones, and are certainly necessary to adapt to a new scientific environment, but one dimension of this which Jones doesn’t discuss as much are the technological (the focus of this blog!) innovations which can help further research in this brave new world.

• Improving science communication with the public. We’ve made multiple mentions of this in the past, but they are no less true here. Active public communications management not only helps secure funding and raise public awareness of the good scientists can do, but it also helps attract the interest of future generations of researchers and policymakers.
• Embracing new collaboration tools. To really kick-start collaboration between scientists across geographies and specialties, we need tools that go beyond just email and fax machines. Tools like Google Wave, wikis, distributed version control, and social media forums like Friendfeed are an early taste of the sort of live collaboration that new web technology can bring about.
• Leveraging cloud computing and heterogeneous compute. One of the reasons discoveries are taking longer and are more expensive is that there is so much more data to collect and to analyze than before. One technological innovation which we’ve talked about at lengths here is the ability of graphics cards/GPUs to make supercomputer-level processing power more readily accessible to research labs. Another is the use of new cloud computing services like Amazon’s to rapidly increase the computational resources that a lab/company has access to. Neither are panaceas for all the data analysis issues which scientists face, but they are definitely ways to make things easier for research groups who have stringent IT budgets.
• Using crowdsourcing to speed innovation. Who says research has to take longer and be more expensive? Perhaps its time to pull on new technological levers which let scientists borrow on the resources and brains of a wider group of people. While new platforms like ChemBioConnect, distributed computing systems like Folding@Home, and volunteer crowdsourcing initiatives like Fold.IT are far from perfect, they hint at a future where researchers can call on resources beyond what their personal computers and brains are capable of.
• Building new research attribution models. When I say new attribution models, I’m referring to two things. The first is embodied by new standards like ORCID which make it easier to understand which person is the author/researcher in question (something which will become more and more important as more people with the same initials/names enter the sciences). The second, and more substantive, is finding new ways to understand who contributed what to a particular study. In today’s digital age, I find it laughable that we still rely on simple author list order to determine the relative roles and positions of the researchers listed on a publication. Employing metadata and other graphical cues can help scientists achieve the recognition they deserve, as well as provide appropriate incentives for teams of researchers to contribute.
• Contributing negative and after-publication results to open repository. While I can understand the hesitation for most research groups to pursue a pure open access strategy, those concerns should not hold with negative or post-publication experimental data. While opening up access to data from failed/negative experiments does little to hurt a lab’s ability to publish first, it can be a dramatic boon for other research groups (especially new labs or labs with interdisciplinary focuses) who can not only use the data for their own analyses and experimental designs, but avoid committing resources to experiments which have already been conducted. If it can work for biotechs and pharma companies, then there’s no reason it should be any different for non-commercial groups.

These suggestions only scratch the surface of what new technologies and policies can do to help scientists in a world where scientific training takes longer and where scientific discoveries need to be more collaborative. If anyone else has any other suggestions, feel free to leave them in the comments!