A day out at 10 Downing Street

Yesterday, I attended a meeting at 10, Downing Street with Dominic Cummings, special advisor to Boris Johnson, for a discussion about science funding. I suspect my invitation will be regarded, in hindsight, as a mistake, and I hope some hapless civil servant does not get into trouble over it. I discovered that I was on the invitation list because of a recommendation by the eminent mathematician, Tim Gowers, who is someone who is venerated by Cummings. Tim wasn't able to attend the meeting, but apparently he is a fan of my blog, and we have bonded over a shared dislike of the evil empire of Elsevier.  I had heard that Cummings liked bold, new ideas, and I thought that I might be able to contribute something, given that science funding is something I have blogged about. 

The invitation came on Tuesday and, having confirmed that it was not a spoof, I spent some time reading Cummings' blog, to get a better idea of where he was coming from. The impression is that he is besotted with science, especially maths and technology, and impatient with bureaucracy. That seemed promising common ground.

The problem, though, is that as a major facilitator of Brexit in 2016, who is now persisting with the idea that Brexit must be achieved "at any cost", he is doing immense damage, because science transcends national boundaries. Don't just take my word for it: it's a message that has been stressed by the President of the Royal Society, the Government's Chief Scientific Advisor, the Chair of the Wellcome Trust, the President of the Academy of Medical Sciences, and the Director of the Crick Institute, among others. 

The day before the meeting, I received an email to say that the topic of discussion would be much narrower than I had been led to believe. The other invitees were four Professors of Mathematics and the Director of the Engineering and Physical Sciences Research Council. We were sent a discussion document written by one of the professors outlining a wish list for improvements in funding for academic mathematics in the UK. I wasn't sure if I was a token woman: I suspect Cummings doesn't go in for token women and that my invite was simply because it had been assumed that someone recommended by Gowers would be a mathematician. I should add that my comments here are in a personal capacity and my views should not be taken as representing those of the University of Oxford.

The meeting started, rather as expected, with Cummings saying that we would not be talking about Brexit, because "everyone has different views about Brexit" and it would not be helpful. My suspicion was that everyone around the table other than Cummings had very similar views about Brexit, but I could see that we'd not get anywhere arguing the point. So we started off feeling rather like a patient who visits a doctor for medical advice, only to be told "I know I just cut off your leg, but let's not mention that."

The meeting proceeded in a cordial fashion, with Cummings expressing his strong desire to foster mathematics in British universities, and asking the mathematicians to come up with their "dream scenario" for dramatically enhancing the international standing of their discipline over the next few years. As one might expect, more funding for researchers at all levels, longer duration of funding, plus less bureaucracy around applying for funding were the basic themes, though Brexit-related issues did keep leaking in to the conversation – everyone was concerned about difficulties of attracting and retaining overseas talent, and about loss of international collaborations funded by the European Research Council. Cummings was clearly proud of the announcement on Thursday evening about easing of visa restrictions on overseas scientists, which has potential to go some way towards mitigating some of the problems created by Brexit. I felt, however, that he did not grasp the extent to which scientific research is an international activity, and breakthroughs depend on teams with complementary skills and perspectives, rather than the occasional "lone genius".  It's not just about attracting "the very best minds from around the world" to come and work here.

Overall, I found the meeting frustrating. First, I felt that Cummings was aware that there was a conflict between his twin aims of pursuit of Brexit and promotion of science, but he seemed to think this could be fixed by increasing funding and cutting regulation. I also wonder where on earth the money is coming from. Cummings made it clear that any proposals would need Treasury approval, but he encouraged the mathematicians to be ambitious, and talked as if anything was possible. In a week when we learn the economy is shrinking for the first time in years, it's hard to believe he has found the forest of magic money trees that are needed to cover recent spending announcements, let alone additional funding for maths.

Second, given Cummings' reputation, I had expected a far more wide-ranging discussion of different funding approaches. I fully support increased funding for fundamental mathematics, and did not want to cut across that discussion, so I didn't say much. I had, however, expected a bit more evidence of creativity. In his blog, Cummings refers to the Defense Advanced Research Projects Agency (DARPA), which is widely admired as a model for how to foster innovation. DARPA was set up in 1958 with the goal of giving the US superiority in military and other technologies. It combined blue-skies and problem-oriented research, and was immensely successful, leading to the development of the internet, among other things. In his preamble, Cummings briefly mentioned DARPA as a useful model. Yet, our discussion was entirely about capacity-building within existing structures.

Third, no mention was made of problem-oriented funding. Many scientists dislike having governments control what they work on, and indeed, blue-skies research often generates quite unexpected and beneficial outcomes. But we are in a world with urgent problems that would benefit from focussed attention of an interdisciplinary, and dare I say it, international group of talented scientists. In the past, it has taken world wars to force scientists to band together to find solutions to immediate threats. The rapid changes in the Arctic suggest that the climate emergency should be treated just like a war - a challenge to be tackled without delay. We should be deploying scientists, including mathematicians, to explore every avenue to mitigating the effects of global heating – physical and social – right now. Although there is interesting research on solar geoengineering going on at Harvard, it is clear that, under the Trump administration, we aren't going to see serious investment from the USA in tackling global heating. And, in any case, a global problem as complex as climate needs a multi-pronged solution. The economist Marianna Mazzucato understands this: her proposals for mission-oriented research take a different approach to the conventional funding agencies we have in the UK. Yet when I asked whether climate research was a priority in his planning, Cummings replied "it's not up to me". He said that there were lots of people pushing for more funding for research on "climate change or whatever", but he gave the impression that it was not something he would give priority to, and he did not display a sense of urgency. That's surprising in someone who is scientifically literate and has a child.

In sum, it's great that we have a special advisor who is committed to science. I'm very happy to see mathematics as a priority funding area. But I fear Dominic Cummings overestimates the extent to which he can mitigate the negative consequences of Brexit, and it is particularly unfortunate that his priorities do not include the climate emergency that is unfolding.

Corrigendum: a word you may hope never to encounter

I have this week submitted a 'corrigendum' to a journal for an article published in the American Journal of Medical Genetics B (Bishop et al, 2006). It's just a fancy word for 'correction', and journals use it contrastively with 'erratum'. Basically, if the journal messes up and prints something wrong, it's an erratum. If the author is responsible for the mistake, it's a corrigendum.

 I'm trying to remember how many corrigenda I've written over the 40 odd years I've been publishing: there have been at least three previous cases that I can remember, but there could be more. I think this one was the worst; previous errors have tended to just affect numbers in a minor way. In this case, a whole table of numbers (table II) was thrown out, and although the main findings were upheld, there were some changes in the details.

I discovered the error when someone asked for the data for a meta-analysis. I was initially worried I would not be able to find the files, but fortunately, I had archived the dataset on a server, and eventually tracked it down. But it was not well-documented, and I then had the task of trawling through a number of cryptically-named files to try and work out which one was the basis for the data in the paper. My brain slowly reconstructed what the variable names meant and I got to the point of thinking I'd better check that this was the correct dataset by rerunning the analysis. Alas, although I could recreate most of what was published, I had the chilling realisation that there was a problem with Table II.

Table II was the one place in the analysis where, in trying to avoid one problem with the data (non-independence), I created a whole new problem (wrong numbers). I had data on siblings of children with autism, and in some cases there were two or three siblings in the family. These days I would have considered using a multilevel model to take family structure into account, but in 2005 I didn't know how to do that, and instead I decided to take a mean value for each family. So if there was one child, I used their score, but if there were 2 or 3, then I averaged them. The N was then the number of families, not the number of children.

And here, dear Reader, is where I made a fatal mistake. I thought the simplest way to do this would be by creating a new column in my Excel spreadsheet which had the mean for each family, computing this by manually entering a formula based on the row numbers for the siblings in that family. The number of families was small enough for this to be feasible, and all seemed well. However, I noticed when I opened the file that I had pasted a comment in red on the top row that said 'DO NOT SORT THIS FILE!'. Clearly, I had already run into problems with my method, which would be totally messed up if the rows were reordered. Despite my warning message to myself, somewhere along the line, it seems that a change was made to the numbering, and this meant that a few children had been assigned to the wrong family. And that's why table II had gremlins in it and needed correcting.

I now know that doing computations in Excel is almost always a bad idea, but in those days, I was innocent enough to be impressed with its computational possibilities. Now I use R, and life is transformed. The problem of computing a mean for each family can be scripted pretty easily, and then you have a lasting record of the analysis, which can be reproduced at any time. In my current projects, I aim to store data with a data dictionary and scripts on a repository such as Open Science Framework, with a link in the paper, so anyone can reconstruct the analysis, and I can find it easily if someone asks for the data. I wish I had learned about this years ago, but at least I can now use this approach with any new data – and I also aim to archive some old datasets as well.

For a journal, a corrigendum is a nuisance: they cost time and money in production costs, and are usually pretty hard to link up to the original article, so it may be seen as all a bit pointless. This is especially so given that a corrigendum is only appropriate if the error is not major. If an error would alter the conclusions that you'd draw from the data, then the paper will need to retracted. Nevertheless, it is important for the scientific record to be accurate, and I'm pleased to say that the American Journal of Medical Genetics took this seriously. They responded promptly to my email documenting the problem, suggesting I write a corrigendum, which I have now done.

I thought it worth blogging about this to show how much easier my life would have been if I had been using the practices of data management and analysis that I now am starting to adopt. I also felt it does no harm to write about making mistakes, which is usually a taboo subject. I've argued previously that we should be open about errors, to encourage others to report them, and to demonstrate how everyone makes mistakes, even when trying hard to be accurate (Bishop, 2018). So yes, mistakes happen, but you do learn from them.

References 
Bishop, D. V. M. (2018). Fallibility in science: Responding to errors in the work of oneself and others (Commentary). Advances in Methods and Practices in Psychological Science, 1(3), 432-438 doi:10.1177/2515245918776632. (For free preprint see: https://peerj.com/preprints/3486/)

Bishop, D. V. M., Maybery, M., Wong, D., Maley, A., & Hallmayer, J. (2006). Characteristics of the broader phenotype in autism: a study of siblings using the Children's Communication Checklist - 2. American Journal of Medical Genetics Part B (Neuropsychiatric Genetics), 141B, 117-122.