If we truly want to foster collaboration, we need to rethink the "independence" criteria during promotion

When I talk about collaborative work, I don’t mean spending a day or two helping compute some p-values and end up as middle author in a subject-matter paper. I mean spending months working on a project, from start to finish, with experts from other disciplines to accomplish a goal that can only be accomplished with a diverse team. Many papers in genomics are like this (the ENOCDE  and 1000 genomes papers for example). Investigators A dreams up the biology, B develops the technology, C codes up algorithms to deal with massive data, while D analyzes the data and assess uncertainty, with the results reported in one high profile paper. I illustrate the point with genomics because it’s what I know best, but examples abound in other specialties as well. 

Fostering collaborative research seems to be a priority for most higher education institutions. Both funding agencies and universities are creating initiative after initiative to incentivize team science. But at the same time the appointments and promotions process rewards researchers that have demonstrated “independence”. If we are not careful it may seem like we are sending mixed signals. I know of young investigators that have been advised to set time aside to demonstrate independence by publishing papers without their regular collaborators. This advice assumes that one can easily balance collaborative and independent research. But here is the problem: truly collaborative work can take just as much time and intellectual energy as independent research, perhaps more. Because time is limited, we might inadvertently be hindering the team science we are supposed to be fostering. Time spent demonstrating independence is time not spent working on the next high impact project.

I understand the argument for striving to hire and promote scholars that can excel no matter the context. But I also think it is unrealistic to compete in team science if we don’t find a better way to promote those that excel in collaborative research as well. It is a mistake to think that scholars that excel in solo research can easily succeed in team science. In fact, I have seen several examples of specializations, that are important to the university, in which the best work is being produced by a small team.  At the same time, “independent” researchers all over the country are also working in these areas and publishing just as many papers. But the influential work is coming almost exclusively from the team. Whom should your university hire and promote in this particular area? To me it seems clear that it is the team. But for them to succeed we can’t get in their way by requiring each individual member to demonstrate “independence” in the traditional sense.

Sunday Data/Statistics Link Roundup (11/4/12)

1. Brian Caffo headlines the WaPo article about massive online open courses. He is the driving force behind our department’s involvement in offering these massive courses. I think this sums it up: `“I can’t use another word than unbelievable,” Caffo said. Then he found some more: “Crazy . . . surreal . . . heartwarming.”’
2. A really interesting discussion of why “A Bet is a Tax on B.S.”. It nicely describes why intelligent betters must be disinterested in the outcome, otherwise they will end up losing money. The Nate Silver controversy just doesn’t seem to be going away, good news for his readership numbers, I bet. (via Rafa)
3. An interesting article on how scientists are not claiming global warming is the sole cause of the extreme weather events we are seeing, but that it does contribute to them being more extreme. The key quote: “We can’t say that steroids caused any one home run by Barry Bonds, but steroids sure helped him hit more and hit them farther. Now we have weather on steroids.” —Eric Pooley. (via Roger)
4. The NIGMS is looking for a Biomedical technology, Bioinformatics, and Computational Biology Director. I hope that it is someone who understands statistics! (via Karl B.)
5. Here is another article that appears to misunderstand statistical prediction.  This one is about the Italian scientists who were jailed for failing to predict an earthquake. No joke. 
6. We talk a lot about how much the data revolution will change industries from social media to healthcare. But here is an important reality check. Patients are not showing an interest in accessing their health care data. I wonder if part of the reason is that we haven’t come up with the right ways to explain, understand, and utilize what is inherently stochastic and uncertain information. 
7. The BMJ is now going to require all data from clinical trials published in their journal to be public.  This is a brilliant, forward thinking move. I hope other journals will follow suit. (via Karen B.R.)
8. An interesting article about the impact of retractions on citation rates, suggesting that papers in fields close to those of the retracted paper may show negative impact on their citation rates. I haven’t looked it over carefully, but how they control for confounding seems incredibly important in this case. (via Alex N.). 

Elite education for the masses

Elite education for the masses

The Year of the MOOC

The Year of the MOOC

Microsoft Seeks an Edge in Analyzing Big Data

Microsoft Seeks an Edge in Analyzing Big Data