Demographics of Destruction — A bonus analysis

Just one extra piece of data analysis following my post on fixing the ARC Discovery Projects scheme. This little chunk ended up on the cutting room floor last night as I couldn’t fully make sense of it. But after 5 hours broken sleep, and some drawing on the shower window with my finger, I think I can explain it.

The analysis is the two dashed linear fits to a sub-set of the ARC’s data shown below.

ARC Tampered

The fits are to % proportion of all CIs in the 10-25 yrs post-PhD bands for male and female, and while I hate fitting a line to three data points the trend is unmistakable. Let’s try to unpack it a bit.

The rise from 0-5 yrs to 5-10 yrs makes sense — this is the next generation coming through into the fellowship stage — and it will be a large demographic fraction due to the ARC’s (worthwhile) recent focus on ECR support and our perverse use of Ph.D. students as a cheap labour force (for another post). This would then make the peak at 10-15 yrs and subsequent drop off attrition by the ‘game of musical chairs’ that happens first in the transition from DECRA to FT and then from DECRA/FT to tenured junior hire. Going forward, I predict this peak at 10 yrs post-PhD to shoot upwards, with the drop-off becoming shorter and sharper. This will essentially be the ‘Superdoc’ effect recently highlighted in Nature.

What is unusual is that this attrition doesn’t continue right through the dataset — if we’re serious about competition in science, shouldn’t we distill and distill so there’s only a few left at the end? Where are all these 25+ year applications coming from? How real is this thing we see in that graph?

Part of the reason I didn’t include this as an ‘appendix’ to the earlier post, is that I now need to start making assumptions to cover missing data — that earlier post is pure data analysis with no assumptions. The key here is to think about age rather than years post-PhD. Now I’m going to assume Ph.D. completion between 25 and 30, I know people will launch an attack on me about mature Ph.Ds, but if you work inside the system, you know those are typically down at <10% level, so bear with me. If you do this…


…you get a column B like the above. What’s going on here is that 25+ years post-PhD ends up being 50+ age bracket, which is demographically broader than the other bands. We really want to compare apples with apples, so in Rows 10-14 I speculate about what that upper cohort probably really looks like. Retirements should kick in strongly from Row 11, and it’s consistent with many years of just ‘looking’ at the ARC outcomes list. Note that I’ve combined gender here, and have taken a gender-weighted success rate per cohort in order to get accurate numbers.

Let’s get back to graphs…

Model 1

Now that we’ve ‘unpacked’ the 25+ year cohort a bit things look more sensible. The green dashed line is ‘ramp-up’ from ECR programs, the red dashed line is a sensible trend for academic attrition due to the game of musical chairs and people finding other things to do. There’s only one place where the data doesn’t fit the trend and it’s in the 45-60 age bracket — I’ve highlighted it with a yellow triangle and will call it the Matthew zone. If you change the distributions in Rows 10-14 this effect doesn’t vanish, it just reshapes slightly (you need a lot of very old scientists getting grants to make it go away).

The glut of late career scientists is obvious, as is their disproportionately large access to available scientific resources (since that all starts with cash). Note once again, I’m purely using CI in any position statistics here and not lead CI or sole CI statistics. As discussed in my last post, this will only exacerbate massively what we’re seeing in the data I’m presenting.

Another way to see this is:

Model 2

where the blue dashed line is retirement attrition and the pink triangle is what I often call the ‘no-future fellowship’ or the ‘valley of the shadows’.

Probably not a lot more to say here unless the ARC is willing to release some sole CI and lead CI statistics so we can know the full story. I don’t know we’ll ever see that happen.

Otherwise, here’s yet more data pointing to ‘the scientific recession we will have to have’ in Australia (to quote Paul Keating), because the next generation are currently being starved at mid-career at the expense of the scientists near the end of their career.

Fixing ARC Discovery Projects

This is a contentious subject, and I’m probably doing this at some personal political risk, but I think it’s a discussion that must be had, and it only happens if someone is brave enougn to kick it off, so here goes. Before I begin, a disclaimer — I’m happy to be corrected about anything written below, particularly if it might improve the transparency of the system and/or promote mature discussion.

The Problem

The problem, as I see it, is a demographic skew in funding that likely comes from many factors, but is one that, in the current super-tight funding environment, threatens to leave us lots of retiring professors, lots of people at DECRA and Future Fellowship (FT) stage, and a wide gulf in between.

The skew is oft talked about amongst more junior researchers, and often claimed to be bogus by those at the top of the system (or ‘anti-meritocracy negativity’), so let me back it with real stats… As my raw data set, I will take the ARC’s own outcome statistics from today (see below):


The statistics state that there were 10769 participants, combined CIs and PIs, of which 2587 (24%) are female and 8162 (76%) are male.The graph is in terms of CIs, not CIs + PIs, so if you add up the percentages, they should add to less than 100% (and do, see below). This enables you to tease out how many CIs there are if you extract the data from their plot accurately enough. Since I like being precise about these things, I’ve chosen to do this using Datathief III… The results appear below (happy to share actual spreadsheet by direct request):


If you add up the percentages measured from the bar graph (Column C), they only add to 93.3% (Cell C24). The missing 6.7% of 10,769 must be PIs, this turns out to 723. Running the percentages extracted on 10,769 participants (Column D) adds to 10,046, with 10,046 + 723 = 10,769.* Column E is Column D recalculated as a percentage of total CIs (10,046) rather than total participants (10,769) — this is vital to getting meaningful data in Column I (see below). Anyway, now that we know the exact number of CIs, we can just pull out all the numbers of funded CIs using the success in band values extracted from the ARC’s own graph (Column F).

Doing so, Column G is the raw number of successful/funded CIs in each gender/age cohort — note this is CI of any position, lead or otherwise, a factor which we will return to further below. In total there are 1788 funded CIs, of which 1310 (73%) are male and 478 (27%) are female. The overall success rate at CI level (not CI + PI) is 17.8%.

Finally, in Column H I calculate the percentage funded in each cohort relative to the total funded and in Column I then look at how this % funded varies from the % of CIs. If this number is negative, then the success rate in your cohort is lower than the overall success rate; and vice versa if it is positive. Note that the value in Column I adds to net zero, as it should: It represents a measure of ‘success’ somehow displaced from one cohort into another relative to what was submitted.

Numbers are nice, but lets look at this in terms of graphs.


I don’t want to gloss over this, so let’s look at the graphs one by one. The pie charts are percentage of CIs (top) and percentage of CIs funded for each cohort (bottom), starting with young men at North and running around clockwise with increasing age. I’ve highlighted male researcher slices with blue borders and female researcher slices with pink borders (this is naff, I know, please forgive me this one). The big result in these pie plots should be obvious to anyone who works in academia — the male:female ratio is massively skewed. To the ARC’s credit (since I know they do put effort against this), the ratio doesn’t get appreciably worse in the carriage from application to funded grant.

From here, the stats are better viewed as bar graphs, both of which are the same data as the pie charts. Comparing the two bar-plots, the most apparent feature is that the percentage of males with 25+ years PhD is the greatest and it is the most appreciably higher relative to the percentage of CIs. The latter is even more obvious when you plot the difference between the percentage a cohort contributes to the funded CIs and the percentage the same cohort contributes to the CIs applying. As mentioned earlier, a positive value here means your success rate as a cohort is higher than it is for all CIs put together, a negative value means it is worse.

If you look at this graph it conclusively proves what many are complaining about — Younger researchers, both male and female, are actually suffering a lower success rate, in real terms, than older researchers, and the real winners out of this are late career males. Now there’s two important things to bear in mind here that make the story my graphs tell look better than what is the true reality:

1. These stats are for CI in any position only and not for lead CI or sole CI (data unavailable — but see Gaetano Burgio’s excellent article on data-mined lead CI stats for DP16 round for more). I’d love to see a deeper demographic analysis on either of these, but my prediction the truth is that lead CI and sole CI grants will be are overwhelmingly dominated by late career males (see plot from Gaetano’s blog below) — this means they have more cash as they are less likely to share it, and if they do share it, they have more control over it. As such, they will gain accumulated advantage that helps them in the heavily track-record dominated (40%) assessment for this scheme. The ability to be lead CI on two DP projects whilst others have none exacerbates this effect.


2. We are not considering the ‘multiplying’ effect of other funding schemes, such as CoE, LIEF, Laureate, etc. Assuming these have a similar demographic skew, it is highly likely that those with a big advantage via Point 1 above also have more cash in general, further accumulating their competitive advantage in this system. There were several late-career male CIs in today’s results who already hold CoE funding, and now have also got DP money as lead CI to add on top.

But let’s consider the converse for a second now. The younger researchers will have less success as a cohort, probably aren’t sole or lead CI, and so have to get what falls off the table from above. If you average this success rate over time — they are more likely to have stretches without winning ARC funding and, at mid-career level, are less likely to get internal funding as they are too senior to get ECR grants and not senior enough to be politically connected or attract the attention of the upper academic hierarchy and get funds ‘off the top’ or outside announced competitive rounds.

The net result of this is a big problem for Australian science. It is what I like to call the ‘no-Future Fellowship’. It’s what you get after your Future Fellowship when you start your tenured middle- career stage, can’t apply for fellowships any more, and suffer a disproportionately low cohort success rate in the ‘open pool’ contest for Discovery grants (for more, see my other post on grant outcome demographics — and the figure below that comes from it). The net result is, that with much of the spoils preferentially going to the late career males, a gap will form behind them, and when they all retire, that gap is going to mean scientific output in Australia goes backwards. In a sense, we’re engineering our own scientific recession that we will eventually have to have….

Model 2

The problem is now pretty clear I think… so let’s look at:

The solutions

I’d like to now speculate on some ways that we can potentially fix these problems in the Discovery Projects system.

  1. Change the assessment fractions — Currently it’s 40% investigators, 25% project quality and innovation, 20% feasibility and benefit, 15% research environment. In other words, 55% of the assessment comes from criteria where accumulated advantage plays a massive role. I would realign the fractions considerably, making them 65% project quality and innovation, 15% feasibility and benefit, 15% investigators and 5% research environment. I would possibly even toss research environment in the bin, because anything more than a tick-box for whether the project is feasible at the institute proposed is just aiming to skew the assessment in favour of higher-ranked universities (i.e., institutional elitism).In the end, ideas are what really matter in innovation, and the best ones should be supported equally, whether you’re a young researcher with a few papers or a senior professor with a h-index of 1000.
  2. Split the Discovery Projects Scheme into two bands: Discovery Senior and Discovery Junior — There is clearly a need to manage the success rates at cohort level in the data above. One way to do this would be to make the proposal go into a separate scheme if any of the CIs on the proposal are 20+ years post-PhD. Another option would be to do this by number of DPs held within the past 10 years, as soon as this exceeds 3, your proposal goes into a separate pool. Alternatively, one could ‘handicap’ the track-record score for all late career CIs — some would argue that ‘track record relative to opportunity’ should do this, but it’s clear in the data above that this is not working.
  3. Go back to the old system of oz/intreaders and rankings over scores — I’m happy to be corrected, but my understanding of the systems, based on many research office info sessions and corroborated heresay is this. In the old system, the rankings that went to the panel meetings were a complex combination of rankings by different levels of readers, with rankings weighted by how many grants a given reader saw. The benefit of this system is that it removes the bias between one reader and another to a decent extent, and is a little less easy to manipulate by readers who read a small handful of grants.The new system of scores has obvious biases in it. Take two grants, one obviously better than the other. One reader might give them an A and a B as they’re a generous marker. Another might give them a C and D because they’re a hard marker. In a system where scores really count, and aren’t weighted heavily by how many grants you read, those two grants above will suffer very different fates (likely only one of them will be funded). One might ask in a ranking system how you tell an A and B from an A and D if you can only say one is better than the other — well that’s why you have some readers reading a lot of proposals and their rankings having a high weight.I think a lot of researchers who have lived through that shift from the old Ozreader/Intreader days to now will know that the system feels much more random, with your outcomes heavily dependent on your ‘luck’ in getting the right or wrong referee. You can have great comments, and still get nothing in the outcomes. An added advantage of the ranking system above over scores is that it is harder for malicious referees to make soft-kills (i.e., pegging the score down slightly, just enough to spike a competitor’s grant without it being obviously anomalous).Hell, I’d almost say that readers shouldn’t score or rank at all. Leave that to the panel who see enough proposals that they can reliably and meaningfully judge the quality of one relative to another. The readers can make their points via their comments, which should be almost entirely focussed on the project and advice on technical aspects beyond the knowledge of a panel member (and probably would be if we implemented Point 1).
  4. Ban anyone who is a CI in a Centre of Excellence from holding any Discovery grant for the duration of funding to the centre — This one is pretty obvious really. You put a bunch of sharks in a pond full of goldfish, and before long you have lots of hungry sharks and no goldfish.
  5. Make CIs only eligible for holding one DP and not two — This will be a controversial one, but let’s think about it for a second. Each year less than 20% of proposals get funded (this year it was 17.8%). This is not because 80% of them aren’t worth funding, quite the opposite, for the 20% that are funded, there’s probably another 30% that are equally good and only further down due to biases in the scoring system, luck with referees, etc — as everyone knows the distribution of quality in grants has a tall narrow peak and that peak sits under the level where the cash runs out so that only the high-side tail of the peak gets any cash before the budget runs out.If we cut the number of DPs held from two to one per eligible CI would it hurt us that much? Probably not, really. More people with great projects would get funded, and they would be more competitive than they would be in a system where they can’t get money (or get it inconsistently) and others continuously have two grants running, year after year, mostly to do closely allied ideas.On that idea of closely-allied ideas, by funding more people to do only their #1 most innovative project, we actually diversify our funding system into more areas, more viewpoints and more mindsets than we have with some doing their most innovative project and another one they can come up with along side it to bring in money and advance their career. Most of these researchers also teach, and with only one DP, they would have more time available to teach better, improving the strength of the students coming into Australian science and leaving to other countries. Some of these researchers also do outreach, which is under-rewarded given it is essential in convincing the public that they should invest some of their taxes in us doing our technical stuff they can’t understand — with only one DP there’s more time for that too. And finally, there’s more time for researchers to have healthy work-life balance if they aren’t permanently chasing or managing two DP grants. As we all know, healthy balance means more creative thinking, which means more innovation. It would also be significantly more family friendly, which matters a lot to the cohorts that have lower proportional success rate in the graphs above!If, at some point, the ARC budget came back to a level where there was more cash available than worthy projects demanding it (unlikely), then one could always revert to holding more than one grant.
  6. Reduce the amount of paperwork involved in applying for grants — My colleagues overseas can’t believe how long our proposals are. My last one was 100 pages for myself plus 2 PIs. Only 10 pages of it were actual science. This is insanity — it means we waste lots of time writing them, especially when the success rate is 17.8%, and it means many international readers won’t assess them as they take forever to wade through. Bear in mind that this disproportionately affects those who have a lower grant success rate. Those who get grant after grant get money for every time they invest in the forms, whilst those who have to fish for years, do more work — this produces an accumulated productivity advantage that skews the system in favour of those cohorts with a disproportionately high cohort success rate (late career males, inevitably).The ARC needs to have a look at best practice overseas. Rarely have I reviewed a grant that’s more than 20 or 30 pages, even with a half dozen investigators on it. The problem in Australia, in the end, stems from track record being such a massive part of the assessment. It inevitably means a CV arms race, with ever growing detail in the forms as people try to engineer the system in their favour via application policy. In the systems I’ve seen with the shortest grants, it’s more about the idea, and a 2 page CV suffices — in those systems the readers don’t even score the track record, they’re just asked to comment on whether the researchers have the ability to do the research or not. It really should be all that matters in a system valuing innovation: sufficient competence not a giant CV.
  7. Once you reach 20+ years post-PhD your track record is entirely about legacy — A slightly more innovative approach might be to make it such that you have 20 years post-PhD where your track-record is entirely measured by the traditional means — what you produce as published output. After 20 years, that gets completely ignored and it’s all about the quality of the people you produce. This would put the onus on the late career folks to repay their success in past funding with enabling the next generation to do science exchange for some slice of the action. This could be combined with Idea 8 below.
  8. Enable the budget to be weighted by CI even between institutions — A major impediment to collaboration in the DP scheme is that there is a budget that all goes to the lead CI’s host institution. As a collaborating CI, the credit you get at your own institution for a grant with another host institute is near zero — mostly because they don’t see any block funding by you doing so. This provides a disincentive to collaborate. However, if you could split the funding up front, say have a UNSW-ANU collaboration where from scratch 50% goes to UNSW and 50% to ANU (or 40/60 or 80/20 decided by the CIs) then everyone’s happy, and if you need to adjust later, you can transfer funds like happens now.The same could happen with senior CIs under Idea 8. They can come on a grant lead by more junior CIs, with a stipulated percentage specified for them to spend. This would ensure legacy building in the next generation whilst keeping senior researchers alive in the system. It would also prevent bullying by ‘silverback’ lead CIs carrying junior CIs to strengthen their proposal in the track-record arms-race whilst giving them little real control in the research once it gets funded.
  9. Properly qualify ‘opportunity’ in the context of track record in the proposal — If we are going to insist on track record being such a large part of the assessment, then I think we need paperwork sections that enable real opportunity to be properly defined. The key thing that should be declared here is exactly how many tenured staff, postdoctoral staff and Ph.D. students you have working under you. It’s easy to have a massively stellar publication output when you are a senior professor with 4 junior academic hires under your control, a half dozen postdocs, 3 technical staff members provided by your university and a small army of Ph.D. students. If you have 3 Ph.D. students and that’s it, getting even close to the same input out is just completely impossible. Internal funds awarded to your projects should, in principle, be declared also.I’ve heard lots of valiant talk about how track record is always ‘carefully considered relative to opportunity’, and find this mystifying because often the precise information that you need to judge that as a reader is never made available. I’d still argue this problem is best fixed via Item 1 (making track record count much less), but failing that, we need to start doing this properly.

It is now nearly 3am, and I can’t think of any more ideas to round out the 10, but perhaps that’s ok. If you’ve read this far, thanks for paying attention to all this. Improving the depth, breadth and diversity of the scientific community is central to innovation. Having a grant system that is skewed to one cohort and/or largely decided by accumulated advantage destroys this. The data I’ve presented, in my opinion, shows this is clearly a problem in the current ARC Discovery Projects Scheme even before you add on exacerbating influences like certain advantaged cohorts being more likely to be sole or lead CI, hold more than one DP, or concurrently benefit significantly from multi-million dollar Center of Excellence funding.

Fixing this problem is vital to maximising the national innovation potential against available finite resources, and the current government should consider it an urgent problem if they are serious about science and innovation in Australia.

For more reading — see also:

  1. “A Note on the Australian Research Council (ARC) Discovery Program” by Gaetano Burgio.
  2. “Demographics of Destruction — A Bonus Analysis” by myself.


* For full honesty, since I believe in it, the spreadsheet actually gives a total of 10,770 in Cell C28, which is off by 1. This comes about because of rounding issues in Cells D10, D21, D24 and ultimately D28, since I need to deal with x% of 10,769 being a real number, and humans coming as integer units :).

We’ve gotta stop worshipping workaholics…

I’ve been wanting to write about this for a while now, and the perfect opportunity has arisen, so it’s time to let rip. Few can have missed the shocking post in Science a few days ago titled “Getting noticed is half the battle” by Eleftherios Diamandis. What I find most shocking, beyond exploiting his wife and neglecting his kids, is that this is actually being promoted as the gold standard for getting into academia!

It’s going to be hard to beat Bryan Gaensler’s excellent counterpiece “Workaholism isn’t a valid requirement for advancing in science” in the Conversation today, but let me talk to it nonetheless…

As Bryan points out, it’s easy to fall into this trap… I fell into it the same way. People there earlier, people there later, people there on weekends, step up your game to try and keep up, before long all you do is work. I’ve been around this vicious cycle twice now — workaholism really is an addiction in many ways, with recoveries and relapses.

I was probably showing inclinations to being a future workaholic during my Ph.D., I’d say most talented students do. But during that time, I was driving myself out of enthusiasm and interest (good) and not expectation, coercion or ‘the arms race’ of academia (bad). I was massively fortunate to have great supervisors during my Ph.D.: I was left to my own hours and while encouraged to push myself also encouraged to be responsible about taking time out. The only time we put in very long hours was the 4-6 week long blocks when our fridges were running — then we worked from 8am-10pm and on weekends simply because experiments cost us about thousand dollars a day to run. We did these blocks once or twice a year, and when we did, we prepared in advance and we’d take a week or two off after it.

Otherwise, we worked pretty normal hours. During the 3rd year of my Ph.D. the group got a new postdoc from Europe — Heiner Linke, who is now a Professor at Lund University. Because of space issues, new students and me writing up, I moved out of the lab (back in those days we had desks in the lab — there’s pros to this) and shared an office with Heiner — it was a very formative experience in my career… I just couldn’t believe how much someone could get into a ~40 hour week, Heiner would come in about 8 or 9, leave about 5, and get much more productivity out of his day than I did. I was more or less writing around the clock at that stage trying to get finished, and with my 60+ hrs a week I didn’t seem to even be close to getting as much done. I was exhausted and unhappy and struggling; he’d just bounce in, get it all done, and be off for a swim at the beach. It was a real eye-opener for me because I realised how much you can get done by being smart about your day — realising this and making it a reality for your daily life are two different things though, I still don’t think I have it mastered (more on this below).

Things got crazy for me around the time I got my ARC postdoctoral fellowship (DECRA equivalent). I’d slipped into the habit of trying to win the arms race by outworking everyone else. At this stage my good role models were gone, largely replaced by people who did the same. You can operate like this for a while and it works, but you can’t do it forever. Come 2007 I had a continuing position and started lecturing, and I was falling to pieces. I was eating take out and junk all the time, I was always feeling off and I had piled on the weight, I was consuming insane amounts of caffeine to switch on after 6 hours sleep, working all day, then drinking way to much to wind down while working in the evenings. I was persistently grumpy and short tempered, hated my job, and getting little productivity our of myself. I wasn’t efficient or effective any more. I was literally ready to write a resignation letter or throw myself under a bus.

Luckily I saw what was going on and managed to turn it around. For a while I pulled the hours right back, forced myself to get daily exercise and eat healthy (I shed 19kg across the next year), got 8 hours sleep a night, dumped the crazy ‘caffeine-alcohol’ merry-go-round, and focused only on doing the work that was essential. It was either that or I walked away and never came back — I had little to lose at that point. Remarkably, by 2008 I was having one of the most productive streaks of my entire career. The ideas where flowing, I was teaching well, I was doing great outreach (all my YouTube work was in that period). I was fit and happy and going places — come mid 2009 I managed to land one of the first round of Future Fellowships, little did I know this would soon bring it all back down again…

The first few years of the fellowship were great, but by 2012 I was falling into old ways again, mostly under the pressure of achieving what’s expected on a fellowship. It’s quite easy to turn your life around when things are bad — everything is shiny and new and interesting, and feeling better drives you forward. But eventually you reach a plateau, and it’s easy to let that little devil on your shoulder, the one that says ‘oh, but you won’t get your next grant if you don’t get this paper’, to talk you into letting little bits of your healthy regime slip away. Before long, you aren’t sleeping enough, you’re working in the evenings or weekends again, etc. and your edge starts go blunt. It takes more time to get less done and the pressure to beat the competition sees you saying yes to more things you don’t want to do. You lose your creativity and your enjoyment of the job. I got here again at the start of this year, and I’m only just recovering again, mostly by being really strict on myself about living good.

The moral of the story: As Bryan’s post says, there really is an optimum here, and if you push beyond it, you start losing your productivity. You need to be disciplined about being balanced at the point that maximises your effectiveness and efficiency.

But I want to return to a point earlier before finishing this post: How do we fix this problem? I see three parts to this.

The first is that we need to change our role models for productivity in science. We should stop worshipping workaholics like Diamandis, whom I’m sure will regret his choices when he gets to later in life and realises how much he lost to his career. We need to replace them with new role models — people who do manage to be highly productive while having a great life. From my earlier discussion, some of you will say ‘Yeah, but it’s easy as a post-doc to work 9-5 and make a Ph.D. student sharing your office think life is all roses, let’s see him do that as a professor’. Funnily enough, I still collaborate with Heiner and he’s still doing it — he’s director of an institute and usually seen getting on his bicycle and heading home at 5pm. He takes his holidays, all of them, and disappears off to his summer house with his wife (who also has an academic career) and kids — people who work with him know you’ll get no email replies in this time. He runs triathlons in his spare time. It really can be done. I think we need more role models like this, and many of us need to join them and make ourselves the example as well… we should let our students see us turn up at 9am, head home at 5pm, work like dynamos for 8 hours, and have a great life in the rest of our week. They need to see that this balance is actually possible, like I was fortunate enough to see myself as a Ph.D. student.

The second is that we need to start passing this insight down. I said earlier that I don’t think I’ve mastered all the skills yet, and I think it’s mostly because I haven’t been trained in how to do it, I just don’t know all the tricks. All I know is what I’ve picked up by osmosis. In modern academia we do a great job of training our ECRs — everyone seems to love giving a course and mentoring at this level. But it seems that once you reach a certain stage, you’re entirely on your own and no one is teaching you anything any more, lest you become a threat to them competitively. I think intense competition is the enemy throughout academia, but more so than ever at the mid-career level, where you’re often left to sink or swim. It’s actually the most crucial stage for a) preventing workaholism, b) preventing the development of supervisors who bully their underlings into the same workaholic behaviour to extract productivity (e.g., the evil Prof. Erick Carriera), and c) setting up role models who can begin to fix this nightmare. I think the Academies and Universities have a serious role to play here in providing training/mentoring on this issue. I sometimes wish I could be a fly on the office wall with people like Heiner or Bryan or Tanya Monro or others who do manage to pull this off and still have a life. I wish these people were being paid to give talks to mid-career scientists about how to get more done in less time. Science Magazine should be interviewing them, and writing articles about their advice for how to get it done, not selling people like Diamandis.

The third is that we need to start actively shunning the behaviour of people like Diamandis, and more importantly, Carriera. Putting your family second to advance your career should be actively discouraged; the sort of bullying behaviour that Carreira engaged in should see people officially reprimanded or fired. This sort of thing still happens (I was shocked to recently hear about an entire lab resigning due to bullying by the lab head, and this was at an Aussie uni too, not the US where this bullying is more common). We need to put an end to it. Ph.D. & honours students should not be getting told they’re expected to work nights and weekends because it helps your arms race; it’s outrageous and people who do this deserve no respect whatsoever. It is because of these arseholes that the rest of us feel pressured to break ourselves and ruin our lives to compete with them.

Ultimately, something needs to change here or science is going to fail. Young scientists are happy to work hard when they are engaged and interested and they should be encouraged to do so in such a way that they are also happy and enjoying life. If all they see is a life of endless hours and unhappiness, they’ll go do something else, where the pay is much better and the hours are more reasonable.

So I encourage all of you: get your balance right, work to be a role model for the right behaviours, help others to get more out of their day wherever possible. Let’s turn science back into what it should be, the most awesomely fun job around.

Why water + E.Coli = superfluid is too good to be true (or the importance of fact checking for science writers)

I woke this cold, foggy Sydney morning to see a tweet that immediately raised a minor blip in my ‘bullshit detector’ (something all good scientists should be equipped with).

Nature Bacteria Superfluidity tweet

Nature Bacteria Superfluidity tweet

Nice click-bait, so I took a look… The first sentence reads “Swimming bacteria can thin out an ordinary liquid and, in some cases, turn it into a zero-viscosity superfluid, researchers report.” This seemed way too ‘good to be true’, my bullshit detector went directly from blip to full-on claxon mode.

Feeling a bit feisty from the cold, I decided to question it… here was the response:

Twitter debate

Twitter debate with author of Nature News article…

I’ll return to this response later, but the viscosity becoming negative was like waving a red rag at a bull… Superfluids don’t have ‘negative’ viscosity; there’s more to this story than is being sold. So, with a big caffeine hit down the hatch (red bull of course), off I went to the journals to look up the relevant (but sadly paywalled) articles.

It wasn’t long before… “Tell me this is one of your simulations… Alright, flush the bombers, get the subs in launch mode. We are at Defcon 1.”

Here’s my response…

A superfluid is a liquid that has zero viscosity and can therefore exhibit dissipationless flow. This means that one can, in principle, start a flow of the liquid and it will flow forever. The classic example is liquid helium-4, which undergoes a superfluid transition at 2.2 Kelvin. Superfluid helium can do some remarkable things like flow up walls to escape a container or through tiny holes that other fluids can’t get through (a nightmare for fridge-jocks like me but that’s another story).

In this latest experiment, the authors are looking at a fluid that is mostly water, but which contains between 0.1 and 1% by volume a population of live bacteria E. Coli that convert chemical energy (food) into directed swimming motion by rotating structures called flagella. Swimming bacteria are a pretty hot topic right now for many reasons extending from how the microscopic molecular motors that drive flagella rotation assemble and operate, through to how collective motions of large populations of these ‘active swimmers’ show complex structure. This research is at the latter end of the spectrum.

Swimming for bacteria is very different to swimming for us humans because of the massive difference in scale. Bacteria live at a size scale where the stickiness of water molecules, and their relentless jiggling due to thermal motion, changes the way the fluid appears to a swimmer — it seems more like swimming in washing machine filled with hot motor oil than a nice calm lake. The result is that the optimum way to swim is very different. If we made a human-sized bacteria and put it in a swimming pool, it would be like a car stuck in the mud, spinning its flagella (wheels) and going nowhere.

What’s different here is the viscosity, which is a measure for how much resistance a liquid shows to a force that tries to make it flow. If a liquid has a high viscosity you have to put a lot of effort into making it flow. A good example of a high viscosity liquid is tar pitch, which is so viscous that it looks like a solid and takes decades to flow through a funnel under gravity (cue link to one of my favourite experiments of all time). Honey is more viscous than water, both are more viscous than air. At the lowest end of the spectrum is liquid helium where, if you make it cold enough, the viscosity suddenly becomes exactly zero.

Back to the topic, which is the bacteria study. The work was done by Hector Lopez and colleagues at Universite Paris-Sud in France and the idea was to measure the viscosity of water with these swimming bacteria in it. The reason is that the stickiness of water at these scales means not only that it changes the way that the swimmers have to swim, but that the swimmers can in turn change the viscosity of the liquid. The way this works is that if there’s some collective behaviour amongst the swimmers, they can drag the liquid with them, making it look, externally, like it flows differently to how it would if the swimmers weren’t there. The big picture here is to try and use measurements of viscosity as a way to look at patterning and structure in collective swimming behaviour in these bacteria. It’s a clever and interesting way to use physics to attack a biological problem.

To help you all understand the experiment, I’m gonna show you the cool spa in my apartment complex (lucky me, hey)….


My spa, which conveniently looks a lot like a rheometer.

The authors use a device for measuring viscosity called a ‘rheometer’ and it looks a lot like my spa. There’s an outside cylindrical ‘cup’ that holds the liquid and an inside cylindrical ‘bob’, these are concentric. The cup can be rotated either way at a given speed using a motor (which would make my spa pretty cool fun). The bob is connected to a wire that enables a rotational force (torque) to be applied. The idea is, you start the cup rotating. The resistance between the liquid and the cup wall will make the liquid try to flow with it. Eventually this flow will start to rotate the bob with the liquid. You then get the viscosity of the liquid by measuring exactly how much torque you need to apply so that the bob is just at the point where it doesn’t rotate (add less torque it rotates with the liquid, add more torque it counter-rotates — the balance is a little like what you have in the clutch in a manual car). If the liquid has a high viscosity, e.g., tar, this torque to stop the bob rotating is huge. If the liquid is low viscosity, e.g., water, then the torque is lower, and if it’s a superfluid, then the torque is zero.

Pretty easy experiment right. But there’s a really deceptive aspect to it that the authors have exploited to ‘sell’ their paper, and the editors and journalists bought it hook, line and sinker (or should it be hook, link and stinker?). I know this hype originates with the authors and not the editors as the final paper has the same title they used on their submission on the PRL submission date (where you can see the body of the article for free…).

Imagine you now take my spa and put 6 children in it. Kids being kids, they very soon work out that if they run around and around in a circle in the spa they can make the water flow around and around, they can then stop swimming and let the water carry them around the spa (I’ve done this, it’s cool fun!). If you then run around and around in the opposite direction, you can make that flow stop and reverse direction. Of course, if the kids just go in all sorts of silly directions, then nothing much happens. You can imagine that if the kids did this while you were trying to use this spa as a rheometer to measure the liquid viscosity you’ll get some weird results — the liquid might look like it has zero viscosity or even negative viscosity.

The behaviour above is exactly what the researchers are trying to look at, just using bacteria rather than children. If the bacteria swim collectively, then the viscosity will change from that of just water — this is fine, it just depends what you try to say about it as a conclusion, and this is where all professional scientists (and professional editors and professional science writers) know that you need to be very careful or people will call you out, and rightly so, because accuracy is everything in science.

The big question here is: if there’s some bacteria in your liquid that do a collective motion that make your rheometer measurement look like the liquid has zero viscosity, is it fair to call it a ‘superfluid’? I think most physicists would argue that the answer is in fact no. If you don’t give the bacteria ‘food’ then they don’t swim. If they don’t swim, then the viscosity is not zero. So, what you have to do here is pump energy into the system in order to keep the liquid flowing as though it has zero viscosity — but then how is it a dissipationless flow fitting the definition of superfluidity? Well it isn’t, you’re just being deceived — there’s just an agent in your fluid that’s hiding the viscosity. To highlight this, let’s imagine a twist on this experiment for a second…

Imagine that we put our researchers behind a wall where they can’t see their rheometer. What we then do is sneak in and put a very thin perspex cylinder with a radius that’s halfway between that of the cup and the bob into the spa. We now arrange that the cylinder can be rotated such that when the liquid outside it is made to flow by the rotating cup, the cylinder is rotated such that the liquid inside the cylinder either doesn’t flow at all, or flows backwards. The experimenters outside would be blown away, what they’d see is a zero viscosity, or a negative one if the inner flow is backwards. But there’s a dissipation going on that they can’t see, and it originates in our added perspex cylinder. Now for the death punch — the cylinder we have here, it’s just a proxy for the collective motion of the swimming bacteria. As far as the essential fluid dynamics is concerned, they are completely interchangeable.

So in the end, we’re all being deceived by some physicists who are trying to oversell their work. Let’s be clear, I don’t dispute their data or their experiment — the measurements look correct and the data valid, and they should get what looks like zero viscosity or negative viscosity even, which is just an indicator of energy dissipation into the system by the collective action of the swimmers. But to call this a ‘superfluid’ and actively sell it as such, is an absolute howler — this nomenclature is just plain deception intended to extract impact from the publishing system in my opinion.

Now that we all understand the experiment, let’s look at the moral of the story. It comes back to my twitter conversation with Chris Cesare this morning. He says ‘good enough for the editors of PRL and the scientists, good enough for me’ — no, that’s not good enough at all. Your job as a professional science writer and journalist is to not just blindly buy the sales pitch of some authors trying to hype up their work so they get more impact from it than they otherwise might. And you certainly can’t blame your lack of healthy scepticism on the PRL editors, who also bought the line (probably more the title than the paper) and shouldn’t have. Science is a very dangerous game if we don’t apply our own personal filter of rationality over the results, if we simply go ‘the PRL editors think it’s tops, so it must be’ then you’re being reckless with your own credibility.

Chris seems to be a young science writer, so I wouldn’t want to rip him too hard on this one. But he’s a trained physicist and he needs to keep thinking like one. This should be a good lesson about due diligence in science journalism, not just for him, but for aspiring science writers (and journal editors) everywhere. Do your homework, don’t just buy the hype.

Can we fix academia by disentangling the two core businesses of research and teaching?

It’s been a while between posts. I’ll begin with two caveats. 1. I enjoy writing in stream of consciousness; think fast, write fast, let the warts be topics for discussion. Over-refined arguments are conversation killers. 2. I was an agnostic forced through the catholic school system. I took joy in arguing contrary points to extreme lengths just to see how far I could get defending them. A loss was often a win.

If you don’t like challenging your thinking, don’t read this article. If imperfect arguments drive you nuts, don’t read this article.

Cue 2015. Twitter is full of discussions of #ponzidemia and the unsustainability of academia. The ‘anointed’ professors get money, often for stuff they’re not best qualified for; track record is king. Junior academics are on the breadline, they spend all their lives submitting proposals, only to have all or almost all of them rated highly and rejected. Sometimes they appear the next year, authored by ‘anointed’ professors. We have postdocs and superdocs and probably soon megadocs. We have seas of Ph.D. students, who come in ‘bright-eyed and bushy tailed’, and 4 years later, are burnt out and worried if they’ll even have a future after living the breadline on their measly stipend while working 60+ hours a week. Their future: work 60+ hours a week for 15 years, if you’re lucky, you might have a <5% (and falling) chance at some sort of stable position. Don’t have kids, don’t have a life, don’t settle in any given place or it’s probably game over. Or, just leave, sorry, the university thanks you for the business, be sure to return your graduation garments on time.

Enough woe, we all know it, you all get the picture. How the hell can we fix this system?

Probably not by keeping the status quo. So, what’s the alternative? Is there one? Is there a radical solution? Perhaps… but I think it involves breaking the horrible entanglement between the two core-businesses of a university: teaching and research. I see this historical artefact as a cause of many problems in modern academia.

Before I get to a possible solution, I’ll declare two things. First, the other night I read this great article about the value of monopolies — “Competition is for losers” by Peter Thiel in the Wall Street Journal. It says some really interesting things about innovation in competitive and monopoly environments. Second, I also read this great article about HP labs and the future of computing — “Machine Dreams” by Tom Simonite in MIT Technology Review. Both articles made me realise the massive innovative power that can be achieved when you create an environment where you have a bunch of really smart people put together into well structured teams with a common mission and, most importantly, good strong continuous financial and managerial support. This already happens some times; thinking locally, I see this a little in some of the Australian Research Council Centers of Excellence (ARC CoEs), although they are often too small, too focussed and sometimes too closed/narrow by competition and fund limits. Places like CERN and some of the medical institutes that sit outside universities are other examples.

Science is a different game now — once upon a time you could have an academic, a handful of students, and a bit of cash, let them have their own free ideas and get good to great things out. As science has become more ‘pointy’ you now need bigger teams working with more resources over longer timescales to achieve outcomes of real substance. What happens more frequently now is this: you have an academic and a handful of students rushed to graduate by low stipends and a university seeking cash for ‘on time completion’. The academic is flattened by a teaching load and writing a sea of proposals (mostly full of bureaucracy nowadays) — the students often struggle to get their supervisor’s time, let alone get them to spend half a day in the lab with them when it’s needed (often). Now add a <20% success rate of getting at best half the cash they need to do a proper job. The result is running scaled down ideas, often with the truly innovative bits shaved off, because they’re invariably the most resource intensive parts. The outcome is usually a bunch of undercooked heavily-hyped papers, put out because they must be to maintain competitive track records. The papers often report results that are irreproducible, if not because crucial details are omitted to maintain competitive advantage, then because they are flukey one-offs with low yield. There isn’t a lot of serious innovation in this, nor a lot of intellectual enjoyment — everyone is unhappy, fighting for survival rather than doing the brilliant, edgy science they actually dream of.

The problem is ultimately one of resources, not just money but also time. So… here’s a possible solution (one that would require some serious resolve to implement, admittedly, something few governments or organisations have these days…).

We take universities and we strip all of the research out of them, every bit of it. No university does a single scrap of research under this model — they are training organisations pure and simple. The ‘academics’ employed by a university are there for one purpose — to teach undergraduates and teach them really well. Their ‘core business’ is no longer conflicted by any thoughts of doing actual research. Undergraduates don’t need academics who do research, what they need is academics who teach well, who have time to give them to help them learn the subject. More time than academics have in the current system, where they immediately race off after class, or worse, actively dodge students, so they can focus time on writing papers and grants to further something other than their teaching. As far as undergraduates are concerned, you could even say that having academics do research actively harms them because it robs them of the human interaction they need to learn technical subjects with depth.

Before you all start screaming ‘no research, but how could you!’ bear in mind I’m talking about the academics not the students here. Students doing research is an entirely separate issue — they can access research through internships at the research institutes discussed below. They can also see a little of it in well designed undergraduate lab classes, where one can teach the approach, albeit without doing actual research (i.e., generating new knowledge, which rarely happens for undergraduate classes anyway as the leading edge is too far beyond them and it takes too much time). This will mean you might want academics who’ve done research before joining the university, that’s fine, but they shouldn’t be trying to do two jobs at once.

Some will say ‘but how do we attract students?’ If you talk to undergrads, you’ll find they mostly aren’t attracted by research until they get a couple of years in and are indoctrinated by the system. It certainly doesn’t determine what university they choose to attend when leaving high school beyond our system of deciding that the best universities are the ones with the best research. Students choose to go to University X because it’s the best and they know this from the marketing not from an actual rigorous informed personal assessment of the research (try talking to them about this, you’ll see exactly what I mean — there are only a few exceptions). If we ranked the universities instead purely on teaching quality, and built all the marketing around that, then they’d still behave exactly as they do now. But the nice outcome would be that they choose University X for the right reasons: because it’s the best at what they are going there to get from it — an education. Pulling research out of universities removes this confusion about core business.

We then take the research and we move it into research institutes that are entirely separate to the universities, even if they might be conveniently located (e.g., up the road, or across town). The folks working in institutes are all full-time research; if they teach, it’s the rare ‘guest lecture’ at the nearby university, perhaps one or two a year. The research institutes are proper research institutes, in the sense that they have a specific focus encompassing all expertise in that area for a given nation or state (i.e., they are true monopolies). They have a proper management structure from top to bottom, such that research directions are decided from above by people who know what is/isn’t going to be properly innovative and are then fully and properly resourced to achieve a proper outcome. The funding could be pure government (e.g., like an ARC CoE) or pure industry (e.g., Google/HP) or pure philanthropy (e.g., Victor Chang Institute) or some mix but it needs to be at a level that researchers are resourced well and kept continually active — not like they often are now in academia, where they spend more time begging for meagre funds via massive proposals at atrocious success rates than doing anything else.

Employment at the institutes would be at 5 levels. The entry level is interns — these are masters students from nearby universities seeking experience during their studies with payments by stipend like they are now. The largest ranks would be comprised by junior fellows and technical staff. Junior fellows are what we currently view as Ph.D. students (i.e. freshly completed Masters students) but I would abolish the Ph.D. entirely, it is a historical artefact in this system — one that basically amounts to several years of indentured slave labour and payment of a small ransom to have access to jobs in the sector. This would mean paying these people a proper salary right from scratch, which is only fair given their vital role.

Technical staff are as we have now — less focussed on research itself, more focussed on doing the jobs that are essential to research being done efficiently. The second highest tier would be the senior fellows — they are the postdocs and junior academics of today. The top tier would be the scientific management — they are the professors of today’s system. All of these people are paid on a more continuous scale at a value that’s fair to their contribution and career progression — it seems outrageous to me that Ph.D. students get paid 1/7th of what a professor makes; paying them properly also means we can also expect more of them in terms of capability, professionalism and output.

The junior fellows are on 5 year non-continuing contracts, everyone else is on a 10 year renewable contract with review at 5 years — no one in the organisation has a permanent position including the management. Employment levels are set by management to be sustainable such that the organisation is optimally productive, with stipulations on working in teams, minimum resourcing, reallocation of people between projects/teams and management structure. There are also proper targets for workplace diversity and workload management — staff should work hard because they want to when they feel they should, not as a pre-requisite to survival within the system. This would be enabled by changing the way staff are assessed.

Only junior and senior fellows are assessed based on output and outcomes, but they are judged from a team productivity perspective. Comments like “blah isn’t first or last author enough, therefore they contribute nothing” should never be heard again — teams are never 90% two members and a load of passengers, these sorts of attitudes are absolutely destructive to good collaborative and collegial science. Note that since there is no longer a grant system available to these fellows — funding runs down through the management system much like in industry — career assessment is only really needed internally. This means that performance can be more properly judged and managed, and skills beyond ‘how many first author Nature/Science papers can you get for yourself?’ can be properly valued. It enables people to better survive career breaks, be they for professional reasons or family reasons.

Management are instead assessed by ‘360 degree feedback’ weighted say at 70% from junior and senior fellows (anonymous upward assessment) and 30% at peer and above. It focuses entirely on the extent to which a manager enables the teams below them, and the institute as a whole, to be maximally productive. For management, it is much more about creating a legacy at the junior levels and investing in the future than it is about feathering their own nests or driving their own output. Management are there to inspire, enable and encourage, rather than to slave-drive and claim credit to advance their own metrics.

This model would necessarily mean fewer people in research, but not necessarily reduced employment — there are now two separate systems needing staffing: one devoted to research and one to teaching. In both cases the employees no longer have a major conflict of interest — they either do teaching and do it well or they do research and they do it well. They are not trying to do both, and ultimately doing them to less than the level they can because there are only so many hours in a day and so much cash to go around. On both sides, you will have fewer burn-out victims, destroyed by working insane hours trying to do two jobs really well — chose one, do it properly. In both cases, if at the end of their contract they aren’t doing it well enough, then they go do something else (or move from one system to the other). If anything, there should be encouraged turnover, and perhaps even schemes to cleanly, fairly and properly ‘manage’ people out of the system into other careers, e.g., politics, public sector, etc., where intelligent people who can reason well are sorely needed.

This model should also mean less time wasted on intense competition for dwindling resources — as  “Competition is for losers” by Peter Thiel in the Wall Street Journal argues, we deliberately create national/state research monopolies that are resourced to a level where they can properly go after innovative ideas. This is not to say competition is eliminated entirely — it can go on as a contest of ideas inside the institute — but clear decisions on resourcing are then made. This enables innovation directions to be ‘shaken down’ efficiently, with the best ones properly resourced so they result in proper outcomes, not underbaked outcomes due to resource starvation.

Finally, it means that universities are properly competing on their actual core business, which is teaching the next generation advanced ideas, not some other core business, i.e., research, that happens to be entangled into the same institution by history. I can see how a university needed academics to do both teaching and research in the 1700s, 1800s, and even early to mid 1900s, but in the modern era it’s entirely unnecessary. The leading edge of research is generally so far beyond undergraduate studies that it’s no longer essential to have research and teaching in the same place any more.

In the end, what many academics are really tired of is trying to do two jobs, neither of which one can really ever feel like they’re doing to the fullest of their ability. One focuses entirely on teaching in a university and they are a pariah, their ability to rise the ranks is heavily compromised because of this crazy conflict where university rankings are fuelled by research output over teaching quality. One tries to do research, but often with limited time and limited resources, and again it is a nightmare to retain a competitive edge when improperly and inequitably resourced and hit with teaching loads that often vary between individuals (and sometimes used punitively).

If we really want true innovation, we need to do it properly, and perhaps separating these two competing core businesses is the best way… some of you will note that structures like this already exist in many places. Sure. But I often wonder now if it’s how it should be everywhere…

12 guidelines for surviving science…

I turn 40 in tomorrow and I’ve more or less been 100% devoted to physics since I was 20 (2nd year uni). It’s been a journey with some highs and a couple of very serious lows. Motivated by this recent excellent post on self-care & overwork in academia, I spent some time looking back and thinking about what would I go back and tell my 20 year old self (aside from get your B.Sc. and then go get a real job, one with good prospects & good money) or others at the same stage, e.g., the 2nd year lab students I taught this year. Some are things I’ve learned and managed to incorporate, some are things that I still fail at despite repeated attempts…

1. Put up walls: Despite having an excellent role model for this over much of my career, I still haven’t learned to put up walls to keep work from infringing on life. If you don’t, work will consume you 24/7 and then destroy you (trust me, I’ve been there several times). Sit down and work out a clear plan for when you will work and when you won’t work. When you aren’t working, then do not do any work at all, full stop. Be disciplined about it. Working hard is one thing, working 24/7 is slavery. We should not be admiring workaholics (and I’ve been one), they just destroy the system for everyone by helping step up an insane arms race.

2. Exercise is not negotiable: This one I did learn, but only after several health issues and reaching 80+ kg in weight (BMI = 28). Doesn’t matter what it is, find something, and make sure you get at least a half-hour’s exercise a day, 6 days a week. It’s good for the black dog, it’s good for your health, and it’s good for your ‘romantic agenda’ ;) . It also makes you massively more effective at work.

3. Eat well most of the time, splurge occasionally: Comes part and parcel of the point above, exercise is pointless if you eat truckloads of garbage. That said, you need to enjoy life too. If you mostly eat really good, the odd splurge won’t matter, in fact, it is even better when its a rare treat.

4. Find ways to manage your stress safely: A career in science is going to be stressful, there’s no two ways about it. It will be worse if you let it make you work all the time. Follow Rule 1, and then make sure you have good safe outlets for the stress. Bottling it up is bad and you can’t hide it, it just always leaks out and puts people offside with you. Drugs and alcohol are not the solution either, even though they might fool you and seem so on the short term. If your drug & alcohol use gets beyond being social, start asking yourself hard questions fast.

5. Choose the right people to work with: Only work with people who a) you enjoy working with, and b) are good enough that you can get things done. This should be an AND gate not an OR gate. Working with people you like but can’t get anything done with just isn’t effective. Working with people who are good but you don’t like is a disaster in the waiting. The corollary here is: Don’t work with arseholes no matter how good they are; being good is not justification enough to work with someone.

6. Make sure you have anti- role models too: Lots of people talk about mentors and role models, few talk about the exact opposite. Look carefully, many people who on the surface look respected by the community and highly successful are held in absolute contempt by the people who work with or for them. Watch carefully, they are everywhere. Do what ever you can to not become like them; they will teach you more lessons than any positive role-model or mentor ever can. Just because someone has outstanding research metrics, doesn’t mean they’re worthy of your respect. Great people are worthy of respect, some of them also have great research metrics, some don’t.

7. Be an all-rounder: A true academic/professor cares about all parts of the job, not just the research. Put effort into your teaching, public engagement and admin tasks, no matter how much it seems your employer only values you for your research metrics alone. You will be far happier for it because you will feel valued even when your research career is at a low point (these inevitably come sometimes). The appreciation of your students or the public is worth far more than any accolade or paper. If all you do is work to receive attention from elitists and the little ‘old boys clubs’ they create, you are bound for a life of unhappiness.

8. Cultivate a life outside: Make sure you take time to have a life outside of science, and that means a full one not a hollow one. Have friends outside academia, have hobbies outside academia, take your holidays and go places. Don’t let the workload take these things away from you. In fact, do quite the opposite, let people inside science see that you have this separate life and sometimes let them be a part of it. This is the only way we will change the toxic workaholic culture that some have forced upon us by their willingness to sacrifice all for their research career. Respect people for working less and having a life rather than working all the time and reinforcing a toxic environment.

9. Don’t work insane hours: The odd all-nighter is ok when it’s desperately needed, some of my best lab work has been done at 3am, but never let it become a habit. If you are working between midnight and 6am it should be for something truly worth doing and requiring it, e.g., being in the lab on a crucial experiment, working to a funding proposal deadline, if it’s just menial crap like e-mail, then go to bed immediately, no excuses. Politely tell off your colleagues if they are e-mailing you between midnight and 6am (and are in your timezone).

10. Value people for who they are, not what they achieve: Some academics only want to talk to the other ‘serious players’ in their field, or people who have something to offer them, and everyone else isn’t worth the time of day. This is a horrible way to approach life, sadly you will meet these people time and again. Talk to people because they are nice, not because they are good. Talk to students and postdocs, they are almost inevitably nice people who haven’t yet been messed up by the elitist arseholes that pervade science. Value the colleagues you get to work with who are decent people. Be nice to the tech staff and admin staff and even the cleaners — they have hard jobs too. Treat all your undergraduate students with friendly respect no matter how good their grades are. They’re all there to learn, some have it easier than others.

11. Define success correctly: If you stay in science long enough, you’ll realise they system defines success all wrong. The system tries to convince you that it’s all about your h-index, Nature & Science papers and grant income. Success is having a good life, getting to do the science you enjoy alongside it, whilst also passing on your knowledge to the next generation. Some people will be fortunate enough to get good research metrics while doing this, some won’t. The people who have good metrics but gave up the rest of their life to get them are not a success.

12. Science is an extreme sport, take risks: Always remember the words of a friend of mine: “There’s no shortage of work for smart, adaptable people.” Too many people in science are too conservative in my view, mostly driven by a very risk averse funding agencies (there are a few exceptions, though none in Australia sadly). If you aren’t pushing yourself outside your comfort zone then what’s the point of working this hard? There’s no excitement in just doing the same old crap, year after year, to keep up a continuous stream of papers, as many do. Just ask Matthew McConaughey about his endless string of rom-coms… Too many professors just keep submitting the same old grant, which inevitably gets funded, so they can just keep doing the same old science — I have zero respect for them. Challenge yourself with new materials, new collaborations, new ideas, new teams. This might end your career, if so, then so be it. Better to live fast and die young than be square. Respect adventure more than metrics.

And as a bonus, since I always like to break the rules…

13. Try to give more than you take: There’s too much focus in science on only doing the things that directly benefit you. Arsehole referees say things like “the applicant has too many papers where he’s neither first nor last author, I’m ignoring these as clearly they are negligible contributions” (real review on one of my grant proposals); people will squabble over authorship and control of projects as a result, some will even shaft one another. You will meet people who will shirk their refereeing responsibilities or admin responsibilities or other responsibilities to try and get ahead. Forget this, because if you behave just like this, you are part of perpetuating the toxic culture of science rather than fixing it. Give your all to all you can (subject to Rule 1 of course), help more junior colleagues as much as possible, they are the future. Just generally chip in when you can. The world of science only gets better if more of us do this. Trying to out-bastard the bastards just makes the world worse.

Ponzidemia and the academic arms race… Some musings from a burn-out victim

A perfect storm of conspiring events is closing in on science and it is likely to have some serious flow-on effects across the coming decades. To my mind, the perfect storm runs a little like this.

For many decades, societies have been seeking to advance the education of their populace — both to provide highly skilled labour to support high-tech industries, e.g., advanced manufacturing, biotechnology, electronics industries, etc., and to ‘enrich the intellectual value’ of society more broadly. Decades ago most students left high school in year 10 to pursue a trade, with ever smaller fractions going on to senior high school, undergraduate and postgraduate training. Now, vast cohorts go on to complete undergraduate studies, often being trained to levels beyond what they need for their future employment. This is a great thing, many studies have shown the benefit of educated societies, but it has unanticipated consequences…

One flow on effect is a glut of Ph.D. graduates, most of whom came through their 8-10 years of training with the sole ambition of becoming a permanently employed professional scientists. Science is not an endeavour that fits well with traditional market models — the development of knowledge is essential, but in most cases, knowledge is not a commodity that is easily monetized. The connections between a distinct discovery and a marketable product can be long, convoluted and slow to realise. As a result, society has traditionally supported science via taxpayer investment from Government — in a sense this is the ultimate in crowd-sourcing for projects that are investments in a better future for all. But the available funds are necessarily limited, and are dwindling in many modern democracies, where the realities of the ballot box have made politicians more likely to give short-sighted tax cuts and middle-class welfare to buy votes rather than long-sighted investments for the greater good.

The result is a funding pie that is not only shrinking in real terms, but that is being sliced ever more finely as it is spread across a growing cohort of scientists seeking funds. This is where the tangled web of ponzidemia, as I like to call it, has its roots.

One of the most vital resources in academia is people-power; a lab doesn’t run itself, it needs people to do experiments, collect and analyse data, fix equipment, prepare research for dissemination to the public, etc. People cost money, and this is particularly so when they have a Ph.D. under their belts, i.e., they are what we know of as ‘post-docs’. With dwindling funds though, you can’t afford post-docs any more, their salary is 3-5 times what a Ph.D. student gets paid. This provides a big incentive to a) not take postdocs any more, and b) just take on lots of Ph.D. students — it makes economic sense right? Why have 1 person who just finished a Ph.D. when you can have 3 or 5 people who are working to get one for the same price?

The universities love this also — Ph.D. students have to pay tuition and that’s an income stream, postdocs pay you nothing. So in the end, it’s a win-win situation all round, unless you’re a Ph.D. student of course, because then your life is like this… First, you get suckered into doing a Ph.D., often while you’re still too young and naive to realise that your chances of getting a tenured position nowadays are almost zero, there just aren’t enough professor positions available, and your chances of getting a post-doc even are becoming slim. Second, you spend those three years on a scholarship that, compared to any real job in science, is basically a slave wage. In many cities, these scholarships are not far above the poverty line (in real terms) and they are either supplemented by the generosity of their Ph.D. supervisor through a ‘top-up’ scholarship, money earned through casual teaching, a third job, and sometimes all of the above. Third, and in the midst of this, to maintain the ‘revolving door’ of Ph.D. intake/graduation that the universities require to satisfy government bureaucrats, you are often now required to produce the same or much more than a Ph.D. graduate did decades ago but in 3-3.5 years rather than the 4 or 5 years it used to take. Ph.D. students often work extraordinary hours (70-80 hours a week is not unusual sometimes) and take many of the crappiest jobs in the system. Their high teaching and outside work loads often mean that they are pushed out the door with a bare scraping or sub-standard Ph.D., something that gives them no chance of ever remaining in academia. The universities move them on as soon as practicable nowadays so someone else can take their place and governments can boast about how many Ph.D. trained graduates they’re producing despite their lesser quality.

An additional factor in this perfect storm is a strange anomaly in the way universities are perceived. The public mostly sees them as educational institutions, a place where we train doctors and lawyers, engineers and scientists. One would naturally think then that the best universities are where you get the best education, and this is how they are ranked, but this is not how it works at all. When it comes to rankings, the best universities are the ones with the strongest reputation for research — the most Nobel laureates and famous professors, the most papers in high profile journals like Nature & Science, the most grant income, the most exciting discoveries. In the university system, quality of education is a distant second, and in the minds of many, dead last. Indeed, often the best educations are obtained at the not so highly ranked universities. The reason is that at the best universities, the professors are often so busy engaged in the academic arms race associated with research that their teaching is mediocre at best, and often blatantly and shameless neglected (actually, many profs boast about this as though it were some research badge of honour).

To corner the student market, the universities need to be top ranked, and this means they must achieve more in research. They need this to get undergraduates to come, to provide the main revenue stream, and they need this to get postgraduates to come, to make up their slave research labour force. The universities use some fraction of their fees to supplement research incomes because of the crucial importance of research to their rankings, and as a result, their bottom lines. As they accumulate more researchers (more researchers mean more output means higher pushes in the rankings), they are furthering the slicing of the government research funds allocated. This has got to the point where, in many economies, there just isn’t enough money to support this arms race any more unless the supplements to research income using student fees can be maintained. What’s the outcome here? A push by universities in many countries to deregulate fee structures so they can charge more from students; it’s the only way to keep competing in the ranking arms race.

Another flow on effect is ever greater levels of productivity required for individual researchers to get government income to support their research — more people in the system with a shrinking pie means more competition. The key factor in this competition for research funds is number of papers in the top or ‘high-impact’ scientific journals. To the lay person this will seem fair enough — funding should go to the best science — but a second arms race combines with the first here to produce a scientific system that is becoming its own massive productivity killer.

For journals as it is for universities, reputation is king. In a perfect example of ‘what we measure is what we come to define as success’, the journals compete on the basis of a metric called impact factor.  A journal’s impact factor is the average number of citations per paper published for the two preceding years. For a journal, the key to keeping the impact factor high is not to publish a lot or have papers that get cited a lot, it’s to make sure that you focus on publishing papers that get cited a lot in the next two years. This means a) focus on sexy topics that are buzzing with work right now and b) focus on review papers as they get cited a lot on short timescales. Rankings matter to journals because it sets what they can charge in subscriptions to university libraries, and more scarily, it sets what they can charge as the article processing charge (APC) for open access papers (no, APCs are *not* set by the real costs of open access at all, they’re set by what the journal can get away with based on its prestige and impact factor, anyone who thinks otherwise is a fool). This focus on impact factor by the journals has some interesting implications.

The most obvious is that it is severely compromising and crippling an important part of science — the peer review process. The original intention of peer review was to have one or two other scientists not associated with the work read and consider it carefully. The goals were to a) check the research has been conducted within the guidelines of the scientific method and is not misleading or unscientific, b) to check that the work is clear to a typical reader, and c) provide an opportunity for constructive criticism to improve the work or check details. Funnily enough, when you submit to journals that aren’t top-level ‘high-impact’ journals this is often exactly what you get, useful constructive comments on how to make the paper better ahead of publication.

But there’s an impact factor level you reach where the character of these reviews changes quite sharply. The focus shifts instead to subjective judgements about whether the work is ‘topical’ or ‘important’ (i.e., sexy and likely to get lots of citations quickly), or whether it is ‘broad impact’ (i.e., likely to be cited by a wide demographic of researchers). The peer review reports are, 90% of the time, rejections that basically amount to why the work doesn’t deserve to be in such a high impact journal whilst barely considering the technical aspects of the science and in some cases, not commenting on it at all! It’s not that these papers are junk either; they ultimately get published in lesser journals as perfectly good science, sometimes with no revisions at all — they aren’t rejected for quality, they are rejected for prestige reasons in a contest for primacy. In fact, the worst rejections by far are the ones where the referee says the science is fine, it just doesn’t deserve that journal, as that’s when you realise how fickle and corrupted the peer review system has become.

A lay person might go, ‘well, that’s your own stupid fault, don’t aim so high’, but how can you not? Competition being such as it is, you have no choice but to bounce off these journals in the hopes of getting in, because getting one of these papers can make the difference between you getting your next research grant or getting nothing and having to fire post-docs or tell Ph.D. students they can’t finish their project any more (and I have heard of Ph.D. students who have had to abandon their degree because their supervisor can no longer finance them — they really are a labour commodity).

The result is that currently most professors have papers that bounce through several journals (I start at the top and work my way down every time – I have no choice), wasting the time of several referees at each attempt and many months of mucking around, before they find a place to finally be published. The wasted productivity here is substantial — many hundreds of thousands of person-hours a year are wasted on this cycle of submissions and frivolous rejections I’d say.

The next flow on effect is the difficulty of actually getting peer reviewers for a paper, and if you do get them, in getting high quality reviews. As a journal editor, getting reviewers is a curse and the sad reality is that a fraction of the scientific community carries the lions share of the load here. Many researchers review much more than their fair share of papers out of a sense of duty towards science, and many refuse to review at all unless they can push their personal agenda through the process. Sadly, the people who review many punish themselves in the process, because the ones who shirk their responsibilities have more time to use to get themselves ahead in publishing. The cruelty here is that whether you get grant funding or not doesn’t care at all about whether you are a good citizen in terms of your responsibilities to peer review. If anything, it rewards you for being a bad citizen, because all that really counts is high-impact publications.

Then there’s the down right ugly. Misconduct scandals, fraudulent results, cases where editors have ignored referees and published papers anyway because the citation counts are too good to miss (e.g., the Hendrik Schon incident), cases where unscrupulous referees deliberately kill papers to protect their own interests. I won’t say more about this, as I don’t want to be perceived as hanging sour grapes in this blog post; others have said more than enough about this, and many of us have been affected by it (yes, I am one of many sad innocent victims of the Schon scandal — perhaps for another blog post).

So, having dealt with hoards of keen, excited and talented young Ph.D. students whose scientific careers will likely end within years of getting their Ph.Ds, I want to touch on another emerging and disturbing effect of ponzidemia and the academic arms race — the detrimental mental health effects on early-mid career researchers in the system. If you do manage to get a post-doc, the fight to survive long enough to get a tenured position now becomes instantly and crushingly intense. Right at the time when people start thinking about a family or setting themselves up for life, they get smashed with the most extraordinary output expectations. Young researchers need to be highly productive in a topical and sexy area (otherwise you can’t get the high-impact papers that get you grants), they’re often forced to become independent before they are really ready to operate as such, they’re often teaching (for free) as they need that experience to get tenure, often lumped with responsibilities by more senior professors (who want the time to stay competitive themselves).

Pressure can be a good thing in small doses, it makes you pointed and productive, but there comes a point where it becomes destructive. It can be as minor as flagging confidence and falling productivity, but I have seen much worse also, I know many in academia who rapidly develop mental health issues (depression, violent mood swings, etc) from job stress and stupidly long hours (80+ /wk), I’ve seen some spend periods as functional addicts, and there are some who I worry won’t come in any more, and not because they found something else to do, but because they took their own lives (yes, I have lost colleagues before). These problems are descending down into the student cohort also, many of whom are obsessed with their next paper and no longer even caring about the science, or even whether things are done properly. Misconduct at student level is becoming common (and often swept under the carpet), mostly due to the pressure exerted on them to produce by a professor who is under pressure to produce — a conga line of subtle bullying that ultimately has its origin in the ranking contests of the universities themselves.

We really need to do something about this problem, if not for the human toll for the scientific toll. So many young researchers are no longer willing or able to be ambitious and creative any more. Why would you when the project that fails could mean you become uncompetitive and can’t get your next grant or next job? How can you, when the pressure is so intense that you haven’t the time or space to think about anything but getting your next paper across the line.

The solutions are probably best kept for a separate post, but at the core, I see several reforms needed in the coming years:

1. Increased funding from government, in a serious technological society, investment should be at >2.0%, currently Australia is at 1.7% and falling (numbers from Wikipedia, but if it’s good enough for Greg Hunt…).

2. A carefully thought out plan for strategic investment of science funds with a long-term perspective that’s decided outside the democratic political cycle.

3. A breaking of the reliance on Ph.D. students as a cheap labour force for research. The advanced economies should not be treating people as 3rd world citizens.

4. A division of the Ph.D. into two degrees, one with restricted intake and careful selection aimed at refilling academic demands, and one aimed at producing postgraduate trained technologists with a better focus on transferrable skills (much of what’s learned in a Ph.D. is NOT transferrable).

5. Much more efforts on behalf of universities to inform undergrad. science students of career opportunities outside academia.

6. A focus on quality for universities that goes beyond trivial metricisation and league-table games based on research output.

7. A focus on quality for academics that is less one-dimensional and focused on a wide-range of skills that include research, but also teaching, public engagement (essential for convincing the public that they need to give us 2.0%+ of GDP to spend on research in the first place), involvement in public policy, etc. The latter can be on sabbatical secondments that are treated as an equal part of the job description.

8. A focus on ambition over productivity in research — as soon as failure of projects becomes too big a risk you stifle innovation and creativity. Many will admit that you often learn more from a project that fails than one that easily succeeds (I certainly have learnt this). Science needs to be less risk-averse and failure-tolerant in many cases.

9. A better focus on providing time for creative thought and work environments where the competition is friendly not aggressive. Cut-throat competition destroys creativity, destroys collaboration and together these destroy innovation.

10. Sensible structures for workload management in academia. Almost every academic I know (including myself) is a chronic workaholic, this is not at all healthy, for us or science or society.

I think I’ll finish here. My goal is not to solve this problem, one person alone never can, my goal is simply to draw more attention to it with the hopes that eventually, enough people care enough to act together to fix it.