Anyone that knows me knows that one of my favorite phrases is “Don’t be a hero.” I say this in a lot of different settings. For example, when my graduate students come to me with an elaborate experiment, I tell them, “Don’t be a hero”—simple experiments yield simple results that are more 
easily interpreted. This phrase carries over into my home life as well. There are certain things I do fairly well. For example, I love to garden and have a pretty good grasp on what to do with plants, a bunch of dirt, and water. But if a pipe is leaking or the lights go out, I have no clue, and quite frankly, my desire to have a clue about that stuff is minimal. Luckily for me, there are tons of people who do have a clue when it comes to plumbing. So rather than being a hero, I call them up, and they come to my house and fix the problem; crisis avoided.
My approach to science is a very similar one, and I believe it’s truly necessary for us to really advance our knowledge of disease. There are certain things my lab does really well. For example, we are very knowledgeable in the area of early neural development and neural stem cells. We can grow stem cells with the best of them and can efficiently derive neural stem cells and propagate them with our eyes closed. That’s great. But, unfortunately, there a lot of things we are not great at. For example, high throughput sequencing and bioinformatics. Nowadays, generating data is not the problem; it’s the analysis that causes the bottleneck. It seems that with all this new hi-tech sequencing technology, we are generating tons and tons of data monthly, and in order to turn that into something useful, we have to break it down into something that makes sense. Now, I can surely learn this process and try to integrate it into my lab. But that would be heroic, and as you know, I am not a hero. So what do we do? We collaborate with bioinformaticists and mathematicians who do this for a living, and we all come together to move the project along.
I think collaborative science nowadays is more important than ever. In this age of daily advancing technology and dwindling funding sources, researchers that pool their knowledge and resources together are much likelier to move forward faster and more efficiently. Now this all sounds obvious and great, but there are some reasons why groups are hesitant to work together:
1. Ego. Self-explanatory, right? Some people just want to do everything themselves and not to involve others. Those people you can’t change, so there really isn’t a lot to say.
2. Authorship rights. This is a more common concern. For academic research groups, who the first author and last author will be is an all-too-familiar discussion that arises when collaborative projects are initiated. I’m a firm believer that this can always be resolved, but it’s best to have these details ironed out up front and in writing, so that when the paper is ready it will not be delayed
3. Competition. Scientists would probably make great poker players, because they make it a point never to let other labs know exactly what they have going on. Science is so competitive these days that any hint of what labs might be doing could cause another lab to try to do the same thing. Or at least so goes the thought process. When you enter into collaboration, you agree to share unpublished data and experimental designs, with the understanding that they should be used only for the team effort.
I guess I understand all of these points, but it is my feeling that collaborative science is where we need to move if we really want to accelerate discovery and therapeutic generation. There is just way too much to do, with limited time and dollars to get it done. We need heroes, and I’m all for competitive drive, but sometimes it’s best to say “Who can help us get this done?”
So when you find yourself in a similar situation, ask yourself, “Am I being a hero?” If you are, maybe you want to branch out and look for collaboration. Give us a call—we’re always willing to help.
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