Recently, when discussing how to make the transition from academia to industry, someone asked me if you need high impact factor papers to get a job in industry. I started the answer by saying that I am no expert on this. I am just one person who got one job in industry, so I do not pretend at all to know how things work. But, after talking to people involved in hiring other people, I gave the following answer:
Just like having a PhD shows that you can bring a multi-year research project to a successful end, having a first author high impact factor paper also implicitly shows some things – even to recruiters or hiring managers who do not necessarily know much about academia. It shows that you have worked on an important scientific topic, usually with other people from other disciplines, and that you have been very persistent in getting your work written in a persuasive way and fought the battle to get it published.*But at the same time, we all know that there’s a whole bunch of luck involved in getting a high impact factor paper. There are plenty of people who work just as hard, but happen to not show that hypothesis hold true. Or that p-values are lower than 0.05. Or have the ability to do all the experiments that reviewer 3 asks for. Those people may have worked just as hard and have exactly the same experience that is valued outside academia- if not more.
So in that sense, getting to a high impact factor papers is like climbing a mountain, where the getting the paper accepted in that high journal is like the picture taken at the summit. If you show the picture to people, it is immediately clear what you have done to get to the top. But some people were less lucky and they climb the mountain when it is so cloudy that on the picture you can’t even see that they are on top of the mountain. They climbed the same route and may even have had a harder time due to the clouds, but it’s not as easy for them to prove it.
My answer to the person who asked this, was that it you do not have high impact factor papers, you may have to work a bit harder in your CV to point out all the things that you have done in academia. To compare it to the mountain, you may add a map of the trail you took to the top, or you have to explain more about who you climbed the mountain with.
*Alternatively, it shows that you were at the right place at the right time and the grad student who was working on the project decided to leave so you only had to pick up the pieces, massage the data to get to put some stars on your graphs, give them to your BSD advisor who writes a persuasive cover letter and gets the paper right into Nature. That shit happens too, I hear.
You know how a while ago I wrote about how being a post-doc is a bit like hitchhiking in the sense that you're dependent on others to get where you want to be? Today a paper came out with advice on how to get a successful career in neuroscience with the slightly ironic title "The Hitchhiker’s Guide to a Neuroscience Career". The paper says many things that are not very new, like that you need many papers and grants and experience abroad in order to be successful as a neuroscientist (or probably any other type of academic scientist). It does however also raise an important point, namely that if you're an early career scientist and you miss the boat (with grant money) a couple times because of things outside your control, it's very difficult to still be competitive. The authors make the case that more established PIs should recognize these people and help, guide and/or mentor them in order to get a more permanent position. I think this is a very important point and I would love to see this happen.
As DrugMonkey already talked about: a well-known neuroscientist doing non-human primate research has announced that he will stop using monkeys but will switch to rodents instead. DrugMonkey ends his post with this:
This is the point where I am supposed to be telling you to call your Congress Critter.
But I can't.
Logothetis is not the first and he will not be the last.
We have had ample opportunity for biological scientists to see and be motivated to do something about this situation.
They have not done so.
So I would be wasting my breath.
I understand DrugMonkey's frustration with people not speaking up about this, and looking at the political climate in my homecountry, we even have a so-called "Party for the animals" who are very vocal against animal research. In addition, new legislation in the homecountry requires every IACUC-equivalent protocol to be published in non-technical terms and anyone in the EU is allowed to request this information. I have noticed that this makes researchers afraid that their personal information will come out in the open; it is not that difficult to figure out who wrote a specific protocol in such a small country with a limited pool of researchers. I can understand what drove Logothetis to quit his line of research. I have also noticed that I have stopped explaining my animal experiments to the people around me. I have stopped talking about it and I need to reconsider this.
So what can we do? For starters, check out Speaking of Research, who not only share stories of research that has been done using animals, but -more importantly - many facts about animal research. If we, as scientists, have convinced ourselves that the experiments we do on animals are necessary, designed in a way that as sophisticated and least harmful to the animals as possible, with the lowest number of animals possible and because as yet there is no alternative, then we should be able to explain this to our neighbors, our friends and our politicians. And if politicians think it is so incredibly important to understand how the brain works through the Brain initiative in the US and the Human Brain Project in the EU they cannot close their eyes for the fact that we cannot learn everything about the brain from humans but that we need to study some aspects in animals, including non-human primates.
Picture from here http://www.glogster.com/hunterstallard/pro-animal-testing-is-pro-life/g-6mefajvl6s3iskdtuidgia0?old_view=True