Category: Perspectives

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PhD – Positive, Happy, Developments

RightOrWrong1921

When wrong is right part 2

This post follows on directly from my previous discussion of my PhD going wrong. As a brief summary of the previous episode: I ran time consuming simulations that took me around 6 month to design and another 6 months to run. The simulation failed in the end because of a bug in some of the software I was using. Therefore, I had to run them all over again!  That took me one day (at least to relaunch it, the simulations are actually still running). In this post I’d like to focus on the importance of starting to enforce good habits in using computers from the start of your PhD, whether you’re doing bioinformatics or field ecology.

Coding facilitates life. A lot. If I could only offer two tricks to remember they would be:

–Writing function-based scripts: which involves isolating functions (the bits that are actually doing stuff) from scripts in order to be able to reuse/modify them easily for further/new analysis.

–Using version control: which involves saving your work as you modify it and keeping a good track of the history so that when something goes wrong you know exactly which one was the last version that worked and which is the version that bugs.

There are loads of other good tips and many excellent blogs about how to start good coding habits (for example, this one or that one) so I am not going to develop the point here.

I’ll just try to make the point by using a philosophical-historical-dodgy example that convinced me to start coding. Coding is like using a printing press vs. a pencil to write a sentence: I can write this sentence of 71 characters in approximately 16 seconds. And that is, with a pencil. If I had to use a printing press, it would take me one second to input each character in the press (assuming I trained a lot) plus one seconds for actually pressing the sentence. So that’s 16 seconds with a pencil and 72 seconds using the printing press (4.5 times longer). If you’re not that old-school, you will use a computer to analyse your data and what often happens is that it will take you less time to do things “by hand” (e.g. modifying column names, removing rows with NAs, etc…) than to write fancy functions. So why bother?

Well it’s the same as using the printing press, if you just want to write the sentence once, then, sure, don’t bother, but if you need to write it 10 times? The writing would take 160 seconds and the printing takes only 81! Also you’re likely to make typos when copying the sentence with a pencil, but you won’t make any with the press!

And the same applies to your computer analysis. If you’re removing columns with NAs “by hand” it will probably take you less time than writing a function. But what if you have more tables? How can you be sure you didn’t miss any? And on the plus side, if you write function-based scripts, chances are that you already have a function that does remove the columns with NAs from a former analysis.

To follow up with my previous post, applied, to me, this happened to be a salvation! Because I spent 6 months trying to apply bioinformatics good practice, it only took me one day to relaunch the whole analysis! I just had to change the name of the version of the software that was bugged and press enter…

            The process of doing actual science (i.e. from coming up with an interesting idea to submitting the paper) is not a continuous and straight process and it can drastically change at every step and is more about trial and error than about succeeding straight off.

Author: Thomas Guillerme, guillert[at]tcd.ie, @TGuillerme

Photo credit: wikimedia commons

 

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On the writing of a PhD thesis

writing“Writing a [thesis] is an adventure. To begin with it is a toy and an amusement. Then it becomes a mistress, then it becomes a master, then it becomes a tyrant. The last phase is that just as you are about to be reconciled to your servitude, you kill the monster and fling him to the public.” Winston Churchill

I’ve just finished my PhD thesis and thought I’d share some of my opinions on how best to go about writing one. But before we get there I’d like to express my skepticism of the value of writing a thesis as a means to evaluate a budding scientist. I don’t know of any papers in journals that run over a 100 pages but classically this is what was expected of us at PhD level. It’s rare that a scientist writes a monograph. Instead we compose pieces of research that can be explained in around 10 pages. Scientists use mathematics and statistics to make our points, in that way our numbers do the talking so we can afford to be succinct. This is in contrast to students of the arts who typically draw on argument and rhetoric in their works building to a singular point or thesis! But that’s irrelevant to this topic because you still have to write one and many departments are quite flexible with their definition of thesis.

So my first piece of advice is write chapters with the aim of publishing them. You’re training to be a scientist and papers are your currency so keep that in mind. Three or four data chapters with a general introduction and discussion seem to be the way to go. If you have this approach you’ll be able to finish up parts long before the deadline. If you can get papers published, all the better, a peer-reviewed chapter looks very well and will be an improved piece of work for having gone through the process. The final body should be a coherent whole but these are not book chapters in a story. That said be aware of how you want to frame the whole thing.

Try to be concise; it’ll be easier for you to write, easier for your examiners to correct and more attractive to anyone else who wants to read it. There may be some work you did over the course of your PhD that has to get the chop to achieve this.

There’s no problem in seeking help. Science is meant to be collaborative, even more so today. In 2012 only 11% of all papers were single authored. You’ll be able to get much better chapters if you include people who can add a bulwark to any of your weaknesses. Just make sure you do the bulk of the work and properly credit your collaborators where necessary.

Give some thought to the program you’ll use to write up the project. MS Word isn’t the only way. I found assembling the whole thing in LaTeX went quite smoothly because it’s specifically made for writing technical documents. The downside was it was difficult for others to comment on it. There are ways to do this but I was a novice at the time.

Step back from the cult of the busy too. I found giving myself a break from the write up helped me come up with a much better frame for my discussion.

Start early, don’t write much, aim for papers, and use LaTeX. Simple. How’s that for concise?

(The contents of this post are subject to change after my thesis defence)

Author: Adam Kane, @P1zPalu, kanead[at]tcd.ie
Photo credit: http://centrum.org/2014/08/creative-nonfiction-workshop-nov-6-9/

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PhD – Pretty Huge Disaster

Dresden

This is a mini series of two posts about finding positive things in negative results. Science is often a trial and error process and, depending on what you’re working with, errors can be fatal. As people don’t usually share their bad experiences or negative results beyond the circle of close colleagues and friends, I thought (and hope!) that sharing my point of view, as a PhD student might be useful.

If you’re about to do a PhD you will fail and if you’ve already successfully finished one, you have failed. At least a little bit… come on… are you sure? Not even a teeny tiny bit? By failure, I just mean scientific failure here, as if you ran an experiment and the result was… a fail, no results, do it again. There are millions of ways to fail, from errors in the experimental design to clumsiness but in this series of posts, I want to emphasize the consequences of failure more than its causes. I think that it is an important thing to learn and to embrace as a young future scientist, as much as journal rejection and other annoying and common silent academic failures.

During the two first years of my PhD, I went from the idea of quickly testing some assumptions as a starting point for a bigger question to some detailed and time consuming simulations on a detailed part of these assumptions. The time spent appeared to be completely useless scientifically because the analysis failed leading to false negative results and kept me away from going back to the bigger question. Or did it?

When wrong is right part 1

Since the summer of last year, I was working on an intensive computational project. I was running a kind of sensitivity analysis to see the effect of missing data on the phylogenies that have both living and fossil species (that’s called Total Evidence to link back to former posts, here and here). In brief I was simulating datasets with a known (right) result by removing data from it to see how the results were affected. Because of my wide ignorance at the start in coding, simulations and the method I was testing, the project took way longer than planned. And all that was of course ignoring Hofstadter’s law (‘it always takes longer than you think, even when you take into account Hofstadter’s Law’).

The expected result, as for any sensitivity-like analysis, was that as you reduce the amount of data, the harder it would be to get the right results. That wasn’t what I found at all. Instead, my simulations seemed to be suggesting that whatever the amount of data, you never get the right results. Suspicious, I tried to check my simulations and asked advice from competent and talented people that helped me finding caveats in my project. But still, after checking and testing everything over and over again, the simulation results appeared to be the same: the amount of data doesn’t matter, the method just don’t work.

Even though these results were negative, they were intriguing and, if they were right, probably important because of the number of people willing to try the Total Evidence method over the last three years. From that perspective, I presented my results at the Evolution 2014 conference in Raleigh. There, I got even more comments from even more people but still, the results appeared to be right. Until one person that had a similar unexpected result suggested that should try an older version of some of the software I was using.

It appeared that person was right and all the weirdness in the results that I tried for months to fix, check and explain were caused by a bug in the latest update (don’t use MrBayes 3.2.2 for Total Evidence analysis, prefer the version 3.2.1).

After an obvious moment of relief, came an obvious negative feeling of having lost my time and how I should have given up instead of continuing to dig. But a posteriori, I’m actually glad of this misadventure and learned two really important lessons: (1) published software is not 100% reliable; always test their behaviour; (2) there is nothing more productive than sending your work to colleagues and experts for pre-reviewing. Even though, the bug appeared to be “trivial and easy to fix”, the amount of comments I had definitely helped improve both my understanding and my standards for this project.

Author: Thomas Guillerme, guillert[at]tcd.ie, @TGuillerme

Photo credit: wikimedia commons

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What has nature ever done for us?

dogs watching tvAnti-environmentalists and apathists often ask why bother to conserve nature – what does it do for us? Cue enthusiastic green arm-waving and heavy sighs from environmental scientists and ecologists who have faced this attitude their entire careers.

Nature is undeniably important for the human race – we wouldn’t be here without plants fixing the sun’s energy into carbohydrates and producing oxygen as a by-product, we wouldn’t be able to grow any food to eat without the myriad of organisms which create and maintain the soil, and exposure to nature has numerous psychological and physical benefits for our health. And yet, it is not valued in political decision-making. The environment, particularly the living biological part of it, is a “cross-cutting” issue which means it’s ignored by most government departments, including those that should be valuing it the most (e.g. Department of the Environment, Community and Local Government; Department of Agriculture Food and the Marine; Department of Communications, Energy and Natural Resources). This is because most decision-making is driving by economics.

International momentum has been building for governments, businesses and organisations to begin valuing nature. This doesn’t just mean putting a price on nature – it’s not all about price-tags – but valuing natural capital in the same way that any other capital (financial, human, built etc.) would be valued. And accounting for this capital in decision-making processes at all levels (from individuals up to government policy).

Some countries and individual corporations have made good progress with this (e.g. the UK has a Natural Capital Committee, Coca-Cola and Puma have famously adopted Natural Capital Accounting systems), but there has been little progress in Ireland, until now.

In April, the first Natural Capital Ireland Conference was held, which brought together academics, government representatives from national and local levels, government organisations, NGOs, business and finance and other stakeholders. The point of the meeting was to try and increase understanding of valuing nature nationally, and progress natural capital accounting at all levels.

The report from this conference is now available on the Natural Capital Ireland website (www.naturalcapitalireland.com) and will be launched at the EPA Environment Ireland conference. In addition, the EPA and NPWS have been working with the conference organising committee to create a national Natural Capital Forum.

Whether this will bump the natural environment up the political agenda, and increase people’s interest and enthusiasm for nature remains to be seen… But we need to keep trying to convince people that nature is important, and that not having it is more expensive and economically damaging.

Author: Jane Stout, stoutj[at]tcd.ie

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Un-reclaiming the name – I am not a zoologist

zoologist

[Disclaimer – this is just my opinion. I do not speak for everyone at EcoEvo@TCD]

Recently on Twitter there has been a call to “reclaim the name” of Botany accompanied with the hashtag: #iamabotanist. The response has been really cool – lots of different scientists working on different questions have posted pictures of themselves on Twitter, often with their plants. It’s amazing the diversity of researchers out there who identify as botanists.

But why try to reclaim the name Botany? The issue is that Botany as a discipline is seen as rather old-school and irrelevant to current scientific challenges. For these reasons it tends to be unpopular with undergraduates and also with university governing boards. More and more Botany departments are being closed or merged with other departments, and Botany courses are being revamped and renamed to attract more students. Zoology departments are suffering similar fates. Like Botany, Zoology is considered an outdated discipline. It tends to fare better with undergraduate students because there are always people who want to work in a zoo or think they might get to cuddle a panda!

I appreciate what the #iamabotanist campaign was trying to do, but I’m not sure I agree. I work in a Zoology department, but I am not a Zoologist. This isn’t because I think Zoology is irrelevant as a discipline, it’s because I’m far more interested in the questions I’m asking, than in the taxa I use to test my hypotheses. Yes, the mammals I work on are adorable and fascinating, but what drives me as a scientist is trying to understand their evolution and ecology, and how the two things are connected. I’ve mostly worked on mammals so technically I’m a mammalogist. I’m happy with this label, but it’s not what I’d call myself if anyone asked. I’d identify as an (macro)evolutionary biologist, or an evolutionary ecologist. I test my ideas on mammals because these are the group I have most data for, but I’m equally fascinated by insects, bacteria, epiphytic plants, parasitic helminths etc. I think we do a disservice to the science if we focus too much on one taxonomic group.

Zoology and Botany at Trinity are particularly diverse disciplines. We have a couple of “classical” taxonomists/systematists, but also phylogeneticists, landscape ecologists, behavioural ecologists, demographers, evolutionary biologists, conservation biologists, developmental biologists and parasitologists. We teach courses across discipline boundaries, and often the person doing research closest to our own is in the other department. But sadly the Botany-Zoology divide still exists, mostly for reasons of history and geography (we are in separate buildings). This is holding back science, rather than pushing it forward.

Maybe we need to identify as botanists or zoologists (or any other taxon specific -ologists) less often, rather than more often. Forcing general questions and principles down taxon-specific lines seems rather backwards. It also isn’t helpful to our students if they only learn about animals and not the plants they eat, or only learn about plants and not the animals they are being eaten by. This interconnectedness is particularly important in light of the challenges of global change and the current extinction crisis.

So in conclusion, I think animals are cool, but I’m not a zoologist.

Author: Natalie Cooper, ncooper[at]tcd.ie, @nhcooper123

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PhD students and the cult of busy

 

busyAcademics often remind me of the Four Yorkshire Men in the old sketch (not actually originally a Monty Python sketch, but famously performed by them in their live shows – comedy nerd out over, carry on), except rather than trying to outdo each with how deprived we were as kids, we’re always trying to outdo each other with tales of how busy we are. We do it so often that it becomes hard to draw the line between how much this reflects how busy we really are, and how much is just “bragging” to assert how important we are. Somehow, we associate importance/success with being constantly busy, and think that good scientists work stupidly long hours and rarely take a day off. This is so inbuilt into our working culture that we feel guilty when we only work 9-5 or have the occasional lazy afternoon!

Worryingly the cult of being busy starts with PhD students. It’s insane the number of times I hear PhD students turning down opportunities (both academic and recreational) because they are “too busy”. Of course there are always going to be periods where you are truly “too busy”. The last few days before you submit a paper, the weeks leading up to a conference, or when you’re in the final stages of writing up. But in general there is nothing in your PhD that is so important that you can’t delay it for a few days/weeks/months. Most times your supervisor won’t mind waiting a few extra days for a draft (they are also busy!), and you can always email journal editors for extensions when writing reviews or returning corrections.

Full disclosure – I was the kind of PhD student that drives me crazy now. I refused to go to seminars unless they were completely related to what I was working on, I rarely read papers for lab meetings, and in my final year I stopped going to morning coffee, ate lunch at my desk and bit the head off anyone who came to my office to chat to my labmates (to be fair this got totally out of control when we got an espresso machine in the office and almost every postdoc in the building came by at least once a day! I’m blaming you Ezard! :P). I regret my tunnel vision now. There were so many things I could have taken time out to learn – things that would have saved me lots of time during my PhD and later in my career. This year I finally taught myself LaTeX for example, which would have saved me months of blood, sweat and tears formatting my thesis. I also wish I’d taken more time to learn to program properly. I’m now working hard to improve my coding, but see that if I’d taken a few months to do this in my PhD, I’d have saved myself a lot of heartache.

I guess my message to PhD students is to try and be less busy, and make more of an effort to enjoy the PhD experience! Easier said than done I know! I am sympathetic – I remember how it felt as a student. I remember feeling terribly inadequate compared to the high achieving PhD students and postdocs around me. I remember the crushing sense of panic and stress as my hand-in date approached and I still hadn’t got past my first chapter. I remember thinking that every hour doing something unrelated to my PhD was an hour wasted. But what I should have known, and what I’ll remind PhD students now, is that your PhD is about so much more than your thesis. Yes, you are judged on your thesis, and you will have to defend it. But you should also be training yourself to become part of the scientific community. Whether you stay in academia or not, it’s pretty unlikely that you’ll ever work on the exact topic of your PhD again. So you’ll need an awareness of other things that are happening in the world of science! You’ll also need to develop other skills, like presentation skills, teaching, and outreach. You can’t do that if you only focus on your PhD topic and nothing else.

But how can we be less busy (and hence less stressed)? This is something I’m constantly trying to deal with myself (if you think you’re busy as a PhD student, don’t ask a Faculty member how busy they are!). A few things I’ve found useful are as follows:

1. Learn to stop when something is “good enough”
Many of the traits that make us good academics, like attention to detail and the desire to do our best at things, can also lead to terribly stressful perfectionism. Instead try to establish when something is “good enough” rather than “perfect”. This is something I’m trying to improve at myself, and I’ll admit that it’s difficult. However, as a PhD student your supervisor should be able to help. Sometimes you can leave stuff like properly formatting references until you’re ready to submit a paper. If you’re really struggling with one section, maybe send the rest to your supervisor for comments, rather than waiting until it’s all perfect (but ask them first).

I find deadlines help me with this – for example, I have a habit of constantly fiddling with lectures so if I have two weeks to make one, it will take me two weeks. However as time goes on, the amount of improvement approaches an asymptote, so two weeks of effort doesn’t create a lecture much better than one that takes me a week. Therefore I give myself strict amounts of time I’m allowed to work on each lecture. After that time passes it’s done. It’s not perfect, but I doubt the students would notice the difference. The same goes for conference presentations and paper drafts.

2. Use “waiting time” efficiently.
PhD students often forget in their rush to finish something and hand it to their supervisor, that their supervisor will take time to return it with comments. If you know this is going to happen you can use that waiting time more wisely. It’s often a good time to format references, add details to manuscript central if you’re submitting a paper, fiddle around with your thesis template etc. Also talk to your supervisor about when they have time to give you comments. There’s no sense in rushing to hand in a draft chapter the day before your supervisor goes on holiday for two weeks leaving you twiddling your thumbs.

3. Schedule time for non-essential reading or for learning skills
As a PhD student I stopped reading widely near the end of my PhD. However, at postdoc interviews I often got asked about what papers I’d read recently that I’d enjoyed. These questions are designed to see how broad your knowledge is, so citing the technical paper you just read on your PhD subject is not going to impress. Additionally, if you want to stay in science (academia or otherwise), you probably should have a basic knowledge of the current controversies in the field. The only way to do this is by reading. However, it’s hard to read non-essential stuff. The easiest way to ensure you do it is by scheduling a bit of time each week (maybe Friday afternoon or Monday morning) to do it. If you choose a time you usually get very little work done it won’t eat into your productivity. I often use this kind of scheduling to learn programming skills or to play with a new R package.

4. Say yes to opportunities!
Of course there’s a limit to how much you can say yes to. But remember that your time as a PhD student is probably one of the most flexible times of your life, especially if you don’t have kids yet. Your schedule is mostly yours to make. So if you can’t get anything to work, spend the day in a local museum and catch up one evening or at the weekend. If you live in a rainy place (cough cough Ireland) and the sun is out, take the afternoon off and go for a bike ride or a walk – you can work a little longer tomorrow when the sun disappears! If you get offered skills training take it, particularly if it’s free and doesn’t require traveling too far. If your friend wants a hand on tropical field work for a couple of weeks, and you have the money, go with them! It’s a great chance to see an exciting country in a whole new light. Go to seminars and conferences. Talk to your colleagues at coffee time. Take a proper lunch break. It’s amazing how much you can get done in short bursts when you need to, especially if you’ve scheduled in proper breaks.

5. But learn to say no to time sucks…
Not everything people ask you to do is going to be useful, and/or fun. If in doubt, speak to your supervisor before saying yes to things (you can then also use the old “my supervisor is an ogre and won’t let me help you, sorry” excuse). For example, organising an event like a conference or an outreach event, is a great thing to have on your CV. But once you have one of these on your CV the gains of organising a second one are low. These things often take up ridiculous amounts of time and energy. The same goes for teaching. It’s great to get teaching experience, but try and get quality experience with different kinds of teaching rather than saying yes to everything. Be strategic in what you spend your time on, based on filling gaps in your CV, and preferably on what you want to do after your PhD.

6. Talk to someone if you need help.
Finally, if you’re really struggling with feeling busy and overwhelmed, talk to someone! Sometimes in academia we have the habit of not talking about problems. This leads us to believe that everyone else is coping, and we’re the only ones struggling. The truth is EVERYONE struggles sometimes. Talk to your friends/PhD colleagues about how you feel – they’ll soon make you feel less alone. Talk to your supervisor, or another faculty member, about ways of coping with stress. And remember most places have a student counseling service if things are too hard to discuss.

Now go forth, be less busy, more happy and more productive as a result!

DISCLAIMER – Your PhD is not all about your thesis. BUT finishing your thesis on time is the most important thing at the end of the day. This post is not about encouraging slacking off, it’s about encouraging efficient working practices. Research has shown that people working 35 hour weeks get as much done as those working 60 hours (long-term, short-term there are gains in working long hours). So use your time wisely, work hard when you have the energy and motivation to do it, and speak to your supervisor if you’re worried about your progress. They are here to help!

Author: Natalie Cooper, @nhcooper123, nhcooper[at]tcd.ie

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Creationism in Science Fiction: Artistic freedom or anti-science?

Black_Hole_Outflows_From_Centaurus

In the late 90s and early 2000s science fiction fans such as myself had a bit of a hard time. As happy as I was when the last few years brought, amongst others, the amazingly crafted science fiction spectacles Battlestar Galactica (2004) and the long anticipated Alien prequel Prometheus (2012), the more disappointed I became when I realised that both events based their storylines heavily on creationism.

Now one might argue that these are works of art and therefore subject to artistic freedom, and generally I do agree. But then again, that wouldn’t be much of a blog post. So let me describe why this is not only such a disappointment, but also cause for a bit of a tummy ache.

First of all, science fiction is not an art form as such but rather a movement within different artistic disciplines. Lots of disciplines use elements from science fiction. It appears in classic literature such as the works of Jules Verne, and the myriad of other authors following his footsteps, and on the silver screen with the most prominent works being Star Trek and Star Wars. More recently (and not surprisingly) science fiction is prominent in computer games and even in music, especially within electronic music with the best example surely being the robot outfits of French house legends, Daft Punk.

Within all these different art forms there must be a common ground, something that can give such different expressions as sound, word or interaction a common name. So what unites all these art forms under the science fiction umbrella? The most straightforward term that we might could come up with is ‘future’, but that would be too simple. Remember that Star Wars took place ‘A long time ago, in a galaxy far away’, but no one would refute that it is one of the defining works of science fiction. I think that the most important element in science fiction is exploration: take the now, look at our social, scientific and technological achievements and make a step forward. Imagine what could happen, imagine a Utopia, a Dystopia, or a world not so different from our own. It can take place on different worlds or on our own home planet. The defining element is that it is a fictional continuation of our current knowledge, exploring whatever implications the creator of the work wants to explore.

And there lies my problem with Creationism. In order to explore the future consequences of our current state-of-art, we must get the state-of-art right. Among the science fiction fans I know it is usually accepted that the general premise in science fiction is theoretically possible, given the knowledge at the time. Of course not everything has to be realistic: the Force in Star Wars adds a mystical element, and that is completely acceptable since it is defined as mystical within the original movies. However, when Prometheus takes an intelligent design approach, it sets an anti-science premise, and builds a future from it, giving the impression that intelligent design is a scientifically valid theory. Battlestar Galactica goes even one step further, bringing God directly into play as the creator. While one could consider this as an example of the previously mentioned mystical element, albeit in a somewhat convoluted way, the story of the show managed to completely mix up mysticism and hard science in a way that is nearly impossible to disentangle, especially for the viewers who do not have a background in science. (It is beyond this blog to summarise the story of a four seasons show, so for the readers interested in more detail I recommend the following essay by Brad Templeton.)

Using creationism or intelligent design as a creator of works of science fiction has two consequences: it disappoints those fans, such as myself, that have a background in science and it gives those fans who do not have a scientific background a false impression of realism associated with creationism, spreading anti-science further.

Of course this is a personal opinion, but I stand to it when I say that creating a work of science fiction should always take the science on which it is based seriously, whether that be promoting a technological future or warning about its perils.

Author: Jesko Zimmerman, zimmerjr[at]tcd.ie

Image Source: Wikicommons

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Learning the art of conferencing

Networking

The start of the new academic year marks the end of my second conference season. I attended two conferences; Evolution 2014 in Raleigh, North Carolina and the British Ecological Society Macroecology meeting at the University of Nottingham. They were at the opposite ends of the spectrum in terms of size and specificity but they were both interesting, useful and, most surprisingly for me, enjoyable.

The difference is that I knew what to expect. Last summer was my first taste of conferences along with the intellectual and social stamina required to last through a day of talks, coffee breaks, poster sessions and post-conference socialising. I was very lucky that I went to each event as part of a group, so there was always a familiar face to find in a crowd and it made networking easier but I still found the whole experience challenging.

This time around was different; the familiarity made everything easier. For a start, I finally figured out how to plan a sensible schedule which didn’t involve running from one room to another between talks. Most importantly, I’m learning how to “play the game”. I’m slowly figuring out how to give an elevator pitch about my research without making the recipient’s eyes glaze-over and how to talk to other people about their research without sounding like a complete idiot. I’m still a long way off from being a proper conference pro but I’m getting there.

Giving a talk instead of a poster made a huge difference. Last year I brought the same poster to two different conferences. Through the course of each poster session, I spoke to a handful of people, some of whom seemed at least partly interested in my research but mostly they were being nice to the student with no one else at her poster. The experience definitely made me into a more pro-active browser at other poster sessions. People usually appreciate some interaction and it’s always more interesting to talk to someone about their research, even if it’s completely outside my area, instead of just admiring their poster design skills.

This year I gave a short talk at both the conferences I attended. It was nerve-wracking but definitely much more beneficial for my research and also my conference experience. I was lucky to have some useful preparation: we’ve often covered presentation skills in NERD club and I benefited from great constructive criticism during practice talks, especially from Thomas and Natalie who could probably have delivered my talk word for word by the time of the conference! It was worth the effort. My presentation was not ground-breaking in terms of the science content or delivery but I was pleased with how my talks went at both conferences. It was also very beneficial to get the feedback, comments and suggestions from the people in the audience who spoke to me afterwards. You get a lot more feedback after telling an audience your research “story” for 10 minutes rather than hoping to attract passers-by to a poster. Furthermore, at a small conference like the macroecology BES group, giving a talk helped to identify me as the “tenrec girl” so I could speed past the normal elevator pitch opening which marks most initial conference conversations.

Some people take to conferences easily; others have to work a bit more. If you’re part of the latter group then I can assure you that everything becomes easier and more enjoyable with a bit of familiarity, practice and experience. I still have a lot to learn but I’m getting there. I’ve realised that many people, particularly the more junior researchers, also find it tricky to master the art of conferencing. So if you’re feeling awkward standing around the coffee counter or sitting in a seminar on your own, chances are that the person next to you is in the same boat and you will both be happier if someone takes the first plunge into conversation. You will add another name to your list of friendly faces in the crowd or maybe meet a new collaborator. In any case, you’ve got nothing to lose so give it a go!

Author: Sive Finlay, sfinlay[at]tcd.ie, @SiveFinlay

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Gender balanced conferences – we all need to try harder!

gender equal opportunity or representation

Recently a conference on Phylogenetic Comparative Methods was advertised online, and quickly the Twitter community noted that all six of the plenary talks were being given by men. Normally my response to this kind of thing would be some grumpy tweeting and then I’d let it go. However, this time was different; I know one of the organisers, several of the plenary speakers are my collaborators and this is the field I’ve dedicated the last eight years of my career to. Therefore I didn’t think I could let this pass by unchallenged. Continue reading “Gender balanced conferences – we all need to try harder!”

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Hard science?

Pure mathematics formulae

Are you a hard or soft scientist?

According to one 19th century science philosopher, the hierarchy of scientific disciplines is strictly segregated with physics and chemistry at the top, biology somewhere in the middle and social science towards the bottom (while astrology and homeopathy must occupy some nether-regions in the bowels of patently unscientific pursuit). The stereotypical view of hard scientists; scrawled equations, incomprehensibly complicated diagrams and lab coats which have definitely seen better days is in stark contrast to their soft scientist cousins; glorified stamp collectors who get into a tizzy over the finer details of the classification of the lesser-spotted something or other. There’s even a hard/soft distinction within biology; surely playing with genes, molecules and machines that go “ping” must be far more scientifically rigorous than conducting habitat surveys or behavioural experiments? I’m not suggesting that societal perception of scientific research is always so limited but the underlying biases, however subconscious, do remain.

The perceived differences between hard and soft seem to stem largely from the degree of mathematical theory underpinning your research. Physics involves maths; maths is hard therefore physics is a hard science. In contrast, traditional biology is merely comprised of learning the parts of the body and possibly frolicking in a few fields. There’s little or no maths involved so biology must be soft. Gross simplifications they may be but I think these views echo the underlying biases of many people, scientists included. They are, needless to say, rubbish.

For a start, maths is inescapable in all sciences. Elaborate equations filled with strange symbols are alive and well in all aspects of biology and social sciences. Ecologists are no more immune from the necessity of mastering mathematical concepts and techniques than their physicist brethren. Admittedly, the prominence of maths does vary according to research discipline; theoreticians are far more likely to be concerned with the finer details of some statistical method than the field biologist applying that method to study changes in squirrel populations. However, to claim that modern research in traditionally softer sciences is any less maths-reliant than the hard sciences is clearly misguided.

So if maths is inescapable does that mean that all science is hard? Well yes. To the extent that all, true science (i.e. hypothesis-driven, testable predictions with proper application of the scientific method) involves maths then yes all science is “hard”. But the application of mathematical techniques doesn’t necessarily deserve the exalted position it receives. Quantitative tests and mathematical models are all well and good but they’re virtually meaningless without real-world observations and predictions as their basis. Maths must be informed by the “softer” side of qualitative scientific observations.

Similarly, maths can be difficult, sometimes even impenetrable when it reaches the upper echelons of some complicated proof for a theory which you don’t understand in the first place. But I don’t think that maths deserves to be seen as any harder than other subjects. Leaving Cert students are now awarded bonus points for passing higher level maths. While the scheme has encouraged more students to stick with higher rather than ordinary level maths it also has the unfortunate effect of perpetuating the view that maths is harder than any other subject. Hopefully the new Project Maths syllabus will help to knock maths off its rarefied pedestal and make it more accessible to students (although the desired effect does not seem to have kicked in yet).

So break down the scientific class structure and don’t be intimidated by the special scientists with machines that go ping. Rather than nonsensical notions of hard vs. soft or hierarchies based on who uses the most complicated maths, it would be far more productive to concern ourselves with the distinction between good vs. bad science. The differences can sometimes be hard to spot but critical understanding of which scientific findings to trust is far more important than any outdated rivalries between the frizzy-haired physicists and glorified beetle collectors of bygone eras.

Author: Sive Finlay, sfinlay[at]tcd.ie, @SiveFinlay

Image credit: Wikimedia Commons