I’ve been thinking a lot lately about academic careers, chiefly because I happen to be involved in some way with three fellowship applications at the moment. For those of you unfamiliar with the academic system at work here, the process is: PhD -> Post-Doc -> Fellowship -> PI (group leader) -> Professorship (Chair). So getting a fellowship is that crucial jump from post-doc to PI and represents a person’s first chance to drive their own research programme. Sounds grand doesn’t it? “Drive your own research programme”. Wow. Who wouldn’t want to do that?
Well, be careful what you wish for. Seriously. I love my job; I love science and I love computers and I get paid to do both. It’s amazing and possibly (for me) one of the best jobs in the world. However, it comes with huge pressures; job insecurity; unparalleled and relentless criticism; failures, both of your ideas and your experiments; and occasionally the necessity of working with and for people who act like awful human beings. It also requires a lot of hard work, and even then, that isn’t enough. This THE article states very clearly and eloquently that very few people actually work an 80 hour week in academia, and you do not need to in order to succeed. I would tentatively agree, though I have pointed out some of the things you need to do to succeed in the UK system, and one of them is working hard.
It’s true, you don’t need to work 80 hours a week in academia…. but you do need to succeed.
What does success look like?
Unfortunately, science lends itself very well to metrics: number of papers published; amount of money won in external grant funding; number of PhD students trained; feedback scores from students you teach; citation indices; journal indices. And probably many more. All of these count, I’m sorry but they do. We may wish for a better world, but we don’t yet live in one, so believe me – these numbers count.
To succeed as a PI, even a new one such as a fellow, you will need to win at least one external grant. Grant success rates are published: here they are for BBSRC, NERC and MRC. I skim-read these statistics and the success rate for standard “response mode” grants seems to be somewhere between 10 and 25%. However, bear in mind that this includes established professors and people with far better track records and reputations than new fellows have. Conservatively, I would half those success rates for new PIs, taking your chances of success to between 5 and 12%. What that means is you’re going to have to write somewhere between 8 and 20 grants just to win one. I couldn’t find statistics for the UK, but the average age a researcher in the US gets their first R01 grant is 42. Just take a moment and think about that.
It’s not all doom and gloom – there are “new investigator” schemes specifically designed for new PIs. The statistics look better – success between 20-30% for NERC, and similar for BBSRC. However, note the NERC grants are very small – £1.3M over 20 awards is an average of £65k per award, and that probably covers you for about 8 months at FEC costing rates. The BBSRC new investigator awards have no upper limit, so there is a tiny speck of light at the end of the tunnel. The statistics say that you will still need to write between 3 and 5 of these just to win one though.
What do grant applications look like?
I am most familiar with BBSRC, so what’s below may be specific to them, but I imagine other councils are similar. Grant applications consist of the following documents:
- JeS form
- Case for support
- Justification of resources
- Pathways to impact
- Data management plan
- Diagrammatic workplan (optional)
- Letters of support (optional)
The JeS form is an online form containing several text sections: Objectives, Summary, Technical Summary, Academic Beneficiaries and Impact Statement. I haven’t done a word count because they are PDFs, but that’s probably around 1000 words.
The case for support is the major description of the research project and stretches to at least 8 pages, depending on how much money you’re asking for. Word counts for my last 4 are 4450, 4171, 3666, and 3830.
The JoR, DMP and PtI are relatively short, 1-2 pages, and mine are typically 300-500 words each, so let’s say 1000 words in total.
Therefore, each grant is going to need 6000 words (properly referenced, properly argued) over 5 documents. They need to be coherent, they need to tell a story and they need to convince reviewers that this is something worth funding.
Given the success rates I mentioned above, there is every possibility that you need to write between 5 and 10 of these in any given period to be deemed a success. In other words, for success, you’re going to need to write often, write quickly and write well. Don’t come into academia if you don’t like writing.
(by the way, there is such a thing as a LoLa which stands for “longer, larger”. These are, as you may guess, longer and larger grants – the last one I was involved in, the case for support was 24 pages and 15,400 words – about half a PhD thesis)
Failure is brutal
I’ll take you through a few of my failures so you can get a taste….
In 2013 the BBSRC put out a call for research projects in metagenomics. We had been working on this since 2011, looking to discover novel enzymes of industrial relevance from metagenomics data. What we found when we assembled such data was that we had lots of fragmented/incomplete genes. I had a bunch of ideas about how to solve this problem, including targeted assembly of specific genes, something we were not alone in thinking about. Reviews were generally good (Exceptional, Excellent and Very Good scores), but we had one comment about the danger of mis-assemblies. Now, I had an entire section in the proposal dealing with this, basically stating that we would use reads mapped back to the assembly to find and remove mis-assembled contigs. This is a tried, tested, and established method for finding bad regions of assemblies, and we have used it very successfully in other circumstances. Besides which, mis-assembled contigs in metagenomic assemblies are very rare, probably around 1-3%. I explained all this and didn’t think anything of it. Mis-assemblies really aren’t a problem, and we have a method for dealing with it anyway.
The grant was rejected. I asked for feedback from the committee (which can take 3 months by the way, and is often just a few sentences). The feedback was that we had a problem with mis-assemblies and we didn’t have a method for dealing with it. Apparently, the method we proposed (a tried and tested method!) represented a “circular argument” i.e. using the same reads to both assembly and validation was wrong. Anyone working in this area can see that argument doesn’t make sense. So our grant was rejected, because of a problem that isn’t important, which we had a method for dealing with, by someone who demonstrated a complete lack of understanding of that problem. Frustrating? I had to take a long walk, believe me.
In 2015 I wrote a grant to the BBSRC FLIP scheme for a small amount of money (~£150k) to get various bioinformatics software tools and pipleines (e.g. BWA+GATK) working on Archer, the UK’s national supercomputer. It’s a cray supercomputer, massively parallel but with relatively low RAM per core, and jobs absolutely limited to 48 hours. The grant was rejected, with feedback containing such gems as “the PI is not a software developer” and “Roslin is not an appropriate place to do software development”. It’s over a year ago and I am still angry.
The last LoLa I was involved in was the highest scoring LoLa from the committee that assessed it. They fully expected it to be funded. It wasn’t, killed at a higher level committee. So even getting through review and committee approval, you can still lose out. One of the reviewer’s comments was that better assembled and annotated animal genomes will only represent a “1% improvement” over SNP chips. I can’t even….
Our Meta4 paper was initially rejected for being “just a bunch of Perl scripts”; our viRome paper similarly rejected for being “a collection of simple R functions”; our paper on identifying poor regions of the pig genome assembly got “it seems a bunch of bioinformaticians ran some algorithms with no understanding of biology”; whilst our poRe paper was initially rejected without review because it “contains no data” (at the time I knew the poretools paper was under review at the same journal and also contained no data).
What point am I trying to make? That failure is common, criticism is brutal and often you will fail because of comments that are either incorrect, unfair or both. And there is often no appeal.
Lack of success may mean lack of a job
It’s more and more common now for academic institutions to apply funding criteria when assessing the success of their PIs: there have been redundancies at the University of Birmingham, as expectations on grant income were set; staff at Warwick have been given financial targets; Dr Alison Hayman was sacked for not winning enough grants; and the awful, tragic death of Stephen Grimm after Imperial set targets of £200k per annum.
To put that in context, the average size of a BBSRC grant is £240k. So Imperial are asking their PIs to win approx. one RCUK grant per year. Do the maths using the success rates I mention above.
Is the 80 hour week a myth?
Yes it is; but the 60 hour week is not. You may have a family, mouths to feed, bills to pay, a mortgage. To do all of that you need a job, and to keep that job you need to win grants. Maybe you haven’t won one in a while. Tell me, under those circumstances, how many hours are you working?
Working in academia (for me) is wonderful. I absolutely love it and wouldn’t change it for anything else. However, it’s also highly competitive and at times brutal. There are nights I don’t sleep. A few years ago, my dentist told me I had to stop grinding my teeth.
It’s a wonderful, wonderful job – but in the current system, believe me, it’s not for everyone. I recommend you choose your career wisely. You don’t need to work 80 hours a week, but you do need to succeed.