Additional Parental Leave: Bit of a damp squib

For once I have a good excuse for not having posted recently: the birth, three weeks ago, of our daughter (Webb2.0, as I like to call her). Now you might expect this joyful event to trigger a cuddly response here, and of course I am chuffed to bits and absolutely besotted with her. However, let’s be honest here: newborn babies are just tremendously irritating. Their constant whinging, at all hours, day and night; their inability to perform even the most basic of biological tasks (the processing of milk and its subsequent disposal as solid, liquid or gas) without inordinate amounts of fuss; and their extreme sensitivity to the slightest suggestion that you’d like them to switch from being cuddled, to lying in their bed – all of these engender a certain grumpiness in this father. So, rather than a Clinton Cards-style paean to the wonders of new life, I am going to have a moan instead. And this particular moan is about how, despite recent developments, we still can’t seem to get parental leave right in this country.

OK, so here’s how parental leave worked when our son was born back in 2010. I got 2 weeks on full pay. The Mississippi^* got a choice: 18 weeks full pay, followed by 21 weeks on statutory maternity pay (SMP, about £135 a week), for a total of 39 weeks; or 12 weeks full pay followed by 12 weeks on half pay, followed by 21 weeks on SMP (45 weeks total). Both options can be extended to 52 weeks using unpaid leave. (See here for full details of my University's policy).

Now this system is fine, quite generous even, but it really accentuates differences between mums and dads, and (especially when you are at similar career stages) this certainly contributes towards the gender gap in scientific careers. In an effort to allow for a more equitable division of parental duties, the government has more recently introduced a system of additional parental leave, which (in theory) allows the leave to be shared between the parents, with up to 26 weeks available for dad.

Great, we thought, and started hatching a plan based on something like: I could take (say) 8 weeks of APL, as 2 days a week spread over 20 weeks. This would mean I could be at home for Thursdays and Fridays, when #1 is not at nursery; we could spend some time together as a family and support each other.

But, having eventually made some sense of the various documents, we realised that sadly the system is not this flexible. Or rather, not at all flexible. In fact when you look into the details this new system starts to look less and less attractive, particularly from a dad’s point of view (and especially if dad’s income is ≥ mum’s). First, you (dad) cannot take any of your APL in the first 20 weeks (when, you know, a bit of extra support might be useful). You cannot take any until mum returns to work (so no chance to spend some extra time together as a family). And you have to take it as a solid block (no chance to spend a couple of days at home every week, then).

Importantly too, as a couple there is absolutely no extra money – any time that you take as a dad will only be paid when it falls within the 39 weeks, after that you’re on unpaid leave. And even within the 39 weeks, you’ll only be on SMP. So if you, as a dad, are the higher earner as a couple you’ll be materially worse off by taking advantage of APL, compared to mum taking all the available leave. It seems to me to be a system designed without consulting those who want to use it. And I honestly can’t see it encouraging many more dads to take more than their statutory two weeks, which surely was the intention.

There are encouraging signs that further refinement of this policy is planned – or at least, Nick Clegg has made encouraging noises about this. Remember those halcyon days of ‘I agree with Nick’? Well, here, I do:

From 2015, the UK will shift to an entirely new system of flexible parental leave. Under the new rules, a mother will be able to trigger flexible leave at any point – if and when she feels ready. That means that whatever time is left to run on her original year can be taken by her partner instead. Or they can chop up the remaining time between them – taking it in turns. Or they can take time off together – whatever suits them. The only rule is that no more than 12 months can be taken in total; with no more than 9 months at guaranteed pay. And, of course, couples will need to be open with their employers, giving them proper notice.

All this is too late for me, of course (no chance of Webb3.0, baring a catastrophic accident…), but it seems obvious to me that this is what we should do. We need to throw additional money at parental leave, and trust parents to sensibly and flexibly apportion this between themselves, over the entire course of the period covered. I am convinced this will pay for itself by helping both men and women to balance family and work commitments more effectively. Comprehensive childcare would help too – in my institution, we rely on the Students’ Union to run the (excellent, oversubscribed) nursery, with rather little interest or support from central University. I would love to see universities more generally take the lead in providing subsidised childcare for all staff and students who need it.

But for now, I am conducting a controlled experiment to test my hypothesis that ‘part time academic’ really is an oxymoron.

^*For some time I’ve been in a quandary as to what to call my partner (we’re not married), as ‘partner’ sounds too formal, ‘girlfriend’ too frivolous, and other alternatives too icky (‘other half’? Nah) or not entirely PC (the Profanisaurus’s ‘bag for life’…). So I settled on ‘the missus’ as light-heartedly affectionate and ironic – promptly autocorrected by my phone to ‘the Mississippi’, which I rather like…

A note on the title: I was sorely tempted to write ‘damp squid’ (which I’ve seen used more than once in print) as it’s such a lovely image; being a stickler for making sense though decided to stick with the correct version…

Zombie stats and hair-trigger outrage: reflections of a Twitter addict

It seems somehow odd to come over all reflective about Twitter, that most impulsive of online communication channels. But over the year or so that I’ve been using it as a kind of super-effective personalised newsfeed, several cautionary tales have played out in my Twitter feed, which I have here distilled into two key lessons. First: distrust numbers, even – especially – those whose implications sit well with your worldview. And second, reign in your outrage: issues are almost certainly more complicated than 140 characters allow. Twitter, by its very nature, gives you soundbites. If you’re lucky, you’ll get a link to something more substantial, but it is so very easy to retweet something that appeals to your sense of how the world works without scrutinising the numbers. My favourite example (not least because it got me onto BBC R4’s More or Less!) is the ludicrous ‘100 Cod in the North Sea’ story that I blogged about last year. Now it takes just a moment’s thought, if that, to realise that this number is very very wrong (by a factor of at least several hundreds of thousands, in fact). But it played so nicely into the ‘overfishing is devastating our seas’ narrative that many people declined to give it that moment, and unthinkingly retweeted.

This is just one example, but my Twitter feed is full of them. I’m interested in the natural environment, and the impacts that we are having upon it, so I follow a range of environmental groups who tend, for instance, to jump immediately on numbers making renewable energy look especially attractive. Now climate change terrifies me, and I am fully behind the idea that we need to decarbonise the economy as a matter of some urgency. But I also agree wholeheartedly with David McKay who, discussing the favourable carbon footprint on nuclear power in his (essential, free) Without the Hot Air, states “I’m not trying to be pro-nuclear. I’m just pro-arithmetic”. Fortunately, the vigilance of people such as Robert Wilson (@CarbonCounter_) provides a corrective to some of these numbers (see for example his dissection of an awfully inaccurate Guardian report on costs to consumers of gas vs. renewables). But such arguments rarely translate so well to Twitter soundbites and so the zombie stats – numbers that we know are wrong, but which are appealing – refuse to die.

What’s the harm in all this? In my post on the cod story I mentioned the fragile trust that now exists between the fishing, scientific and conservation communities which has led to promising progress in the recovery of North Sea cod stocks but which can easily be shattered by the promulgation of laughable statistics. More generally, dubious numbers muddy whatever water they fall into. In his excellent critique of mainstream economics The Skeptical Economist, which I reviewed previously, Jonathan Aldred warns that “dubious numbers are infectious: adding a dubious number to a reliable one yields an equally dubious number”. Which leads me to propose Mola mola’s second law¹:

An argument advances with the rigour of its most dubious number

So it doesn’t matter how watertight the ethical case for regulating cod fisheries, or for moving away from fossil fuels; if you use farcical numbers to advance this case, the argument will fail to progress.

Another consequence of these kinds of zombie stats is the hair-trigger outrage for which Twitter is (in)famous. This applies just as much to the niche worlds of the practice and administration of science as it does to Westminster or celebrity gossip. In particular, it is rare for a day to pass without some call appearing in my timeline to sign a superficially worthy-looking petition. I am extremely wary of doing so, for a couple of reasons.

For instance, a while back I signed something against some kind of reforms (I didn’t read the details) in a European marine institute which I have visited a few times and where I have friends and colleagues. Surely I should support them in their hour of need? Hmmm. Well. Next time I went, my friend – an extremely conscientious and committed member of the institute – said words to the effect of ‘Please, nobody sign that petition. These reforms are exactly what we need and the people fighting against them do nothing here.’ Duly chastised, I resolved to be more discerning in future.

Then there was the curious incident of Nerc’s planned merger of the British Antarctic Survey with the National Oceanography Centre. Now it is my view that Nerc handled this pretty poorly, but although there were some pretty convincing arguments – both scientific and geopolitical – against this merger, there were good arguments for it too, and chatting to people I know at both NOC and BAS (as well as reading things like this excellent coverage from Mark Brandon) just confirmed to me that this was very far from the black and white issue painted by many environmental journalists and pressure groups who backed a petition against it. And the joy which met the announcement that the merger (which is what was proposed, although it was typically presented as the 'dismantling' or ‘abolition’ of BAS) made no mention of the budgetary constraints at Nerc which had prompted the proposal, which still exist, and which will now require that savings be made in some other area of environmental science.

To reiterate: I don’t know whether the correct decision was made. But that’s precisely the point. I know the issues and institutions involved pretty well, yet didn’t feel sufficiently well-informed to decide one way or another. In such a case, it would be hugely irresponsible of me to sign any petition. Yet many thousands of people did. Call me an old cynic, but I doubt all that many of them had read widely on the rationale for the merger. Sadly, it is now far easier to create a petition – let alone sign one – than it is to inform yourself about an issue. Of course, the UK government has made a rod for its own back here with e-petitions initiative and its commitment to debate in parliament any issue that gains 100,000 signatures. But it is our responsibility as thoughtful citizens to take the issue of signing a petition seriously. Which usually means basing our opinions on more than 140 characters of research.

Bearing these caveats in mind, and keeping critical faculties engaged at (almost) all times, Twitter remains for me an essential source of information, conversation and debate, and an invaluable means to publicise work and opportunities, and I encourage non-tweeters to have a go - good guides for sciencey-minded beginners here, here, here and here.

¹First law here

Big data for big ecology

As buzz words go, ‘big data’ is right up there just now. It seems that every question you care to think of, in every field from public policy to evolutionary biology, can be hit with the big data hammer. Add an ‘omics’ or two too, and you’re laughing. So I’m slightly ashamed that we decided to call our workshop at the British Ecological Society’s Annual Meeting ‘Big Data for Big Ecology’. But when I say ‘we’ I mean the BES Macroecology Special Interest Group, and Macroecology is – as its name suggests – ‘big ecology’, so it seemed natural to combine this with the buzz word du jour.

And as it turned out, I think we were vindicated. We held the first of two 1 hour workshops in a room that could comfortably seat 50. Over 100 squeezed in, and we had to turn some people away. So clearly the interest is there, perhaps at least partly because ecological ‘big data’ differ from the data collected in other fields, and we’re still feeling for how best to deal with issues of storage, access, and analysis. This contrasts with some other fields. For instance, sequence data take a pretty standard form, and it’s relatively straightforward to design a system to collate all sequence data – Genbank is testament to this. Ecological data are much more heterogeneous – people measure different things in different systems, there’s no universally agreed common unit of measurement, people work at different spatial scales, in different habitats and environments, and so on. There is also the matter of what we mean by ‘big’. Again, there’s a contrast here with genomics, where a million sequences is now almost a trivially small number. I think in ecology we’re much more likely to be dealing with records in the thousands or hundreds of thousands, so again the computational challenges are different: doing something clever with a large quantity of complex data, rather than with an absolutely huge amount of more simple (or at least, relatively standard) data.

The aim of this first workshop was to introduce a couple of major ecological datasets, then to discuss the issues associated with sharing data. Importantly, by involving figshare, we were able to present some solutions rather than simply rehashing the same old (perceived) problems. I posted a storify of this first hour here, but briefly we heard from Paula Lightfoot, data access officer for the UK’s National Biodiversity Network Trust. The NBN holds >80 million distribution records from around 150 data providers, consisting of almost 800 individual datasets. Data cover a very wide range of taxa, although birds, lepidoptera and flowering plants make up ¾ of the records. The NBN gateway has always been a fantastic public-facing portal to biodiversity data (go and have a play if you want to confirm this), but these data are underused in research. So for me it was particularly interesting to learn about recent improvements to the NBN’s data delivery system to try to address concerns such as those raised by a BES working group involving several of the Macroecology group (including myself and group chair Nick Isaac). Some of the data on NBN is sensitive or otherwise restricted access, but now you can trigger a single request which goes to all relevant data owners. Likewise, you can download information from multiple datasets as a single text file – which, as ecological data analysts, is often all that we want.

Charly Griffiths from the Marine Biological Association data team then gave an overview of the data holdings in Plymouth, which was really valuable I think to raise awareness of some of these phenomenal datasets among the overwhelmingly terrestrial community of the BES. Things like the Continuous Plankton Recorder data held by SAHFOS, which at >80y is among the longest-running and most spatially extensive ecological time series in existence. Or the Western Channel Observatory data, which is one of the very few long-term datasets to collect information across an entire community (“from genes to fish, from seconds to centuries”).

Then we changed tack, from talking about where we might find data, to what we should do with our own. A quick show of hands revealed that almost everyone in the room had used other people’s data in their work; rather fewer had shared their own data. Mark Hahnel from figshare gave a quick demo to show how easy it can be to share all kinds of outputs – from static figures to code to very large datasets – on the figshare platform, where it instantly gains a doi, and thus becomes citable.

Given how easy this process is, why don’t more people share their data? Our discussion identified two main objections. First, people remain highly protective of their data, and suspicious that there are armies of people just waiting for it to become public so that they can do the same analyses (only faster) that the data owner had planned. I think this is understandable – ecological data are often collected in pretty extreme environments, involving a huge amount of hard work, and it is natural to want to get the full benefit of this toil before others are able to profit.

There are two counters to this. First, the idealistic one: in most cases you were paid to collect your data, very often with public money; the data are not yours to hoard; you were not funded to advance your career, but to advance science. Second, more pragmatically: it’s unlikely that many people are especially interested in what you do. Only a small fraction of those who are will have both the time to start to work on your data, and the expertise to do anything useful. Fewer still will be inclined to screw you over, especially (and this is important) if you have taken the step of laying out your stall in public (on figshare or wherever). And academic karma will sort them out soon anyway…

The second issue, that of data ownership, is harder to address, regardless of any mandate to make data available. This is a particular problem for someone like me, who uses other people’s data all the time. The value that I add lies in combining existing datasets and analysing them in novel ways. Often I have had to secure various permissions to use the data in the first place, and the extent to which what I have produced is an original data product is not clear. So while my inclination is to share everything, I do have to be very careful that I’m not sharing anything where I have previously signed an agreement to say that I won’t. Even in these cases though it is still possible to share extensive metadata and the code used to access and analyse the data.

Scott Chamberlain, who delivered the second workshop, touched on some of these kinds of issues, as well as potential solutions. Scott and the rest of the ROpenSci team use APIs to access large datasets, and it is perfectly possible for a data provider to restrict access to their data via this API route. In which case, one can publish a load of R code documenting how data were accessed, manipulated and analysed, which could be replicated by anyone having the same data access privileges that you do (often gained through personal contact with the data provider). This could be a really neat solution to accessing multiply-owned datasets. Scott’s presentation is online here, and if you have any interest in accessing data using R, it is a must read, and highly endorsed by all of the 100 or so of us who were at the workshop (see some of the comments in my second storify).

So where do we go from here? That’s a genuine question: we clearly hit a nerve and got a huge amount of interest, so we want to take it forward. But how? Should we be writing a set of standards for ecological data? A catalogue of existing datasets? A set of tutorials? I appreciate that we are far from the only people interested in this, and don’t want to replicate the efforts of others – so maybe a list of these other efforts would be a good place to start? Any thoughts gratefully received, either in the comments here or via Twitter (@besmacroecol, @tomjwebb, #besbigdata) or our facebook group.