On systematic reviews

This isn’t really a post – sorry about that – more of a note to say ‘look at this post me and Dr Georgina Key contributed to the excellent EcoEvo@TCD blog over here: http://www.ecoevoblog.com/2014/02/19/systematic-reviews/‘.

It’s all about what my time contributing to a systematic review of sustainable practices for working with soil in agriculture. I’d head on over there and have a read if I were you.

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Academia and the infinite horizon

Last week I took part in a very interesting NERD Club (run by @EcoEvoTCD‘s @nhcooper123 – check out their marvellous blog for loads of useful information and perspectives on all things ecology and evolution) session on alternatives to academic careers. In a competitive field, making the choice between a traditional academic career and something that, well, isn’t that, is increasingly becoming a talking point among PhD students and early career researchers. The perceived process of getting a foothold in an academic career is frequently accompanied with expectations of long hours, continual transit between short-term contracts, and much heaping of responsibility – whether these perceptions are correct or not, the result is an increase in conversation about the alternatives.

As I fall into the ‘early career researcher’ box, I can’t really contribute much in the way of hindsight or perspective. However, there were two guests at NERD Club who could – they had both done PhDs and post-docs, and now had non-academic (but, crucially, not non-scientific or non-research) jobs outside of the university circuit. This first fact was interesting in itself, suggesting that, without actually trying a post-doc, it’s difficult to know if you’ll like it or not. If you’ve made the effort to get a PhD, it seems worth the further effort to try the academic route, to decide whether or not it’s for you, unless you’re completely certain that it isn’t for you. My own experience of a post-doc has been quite different to that of my PhD, in ways that I didn’t think it would be.

I got the impression that choosing a non-academic career involved a compromise between the ‘flexibility’ and ‘infinite horizons’ theoretically offered by academic posts, and something with more security, but less autonomy. This post isn’t intended to express a certain position on the issue – rather, I’d prefer it to prompt some discussion. Please do comment below if you’d like to. There were several other useful hints and pointers to come out of the meeting, which I’ll list below.

  • A ‘non-academic’ career doesn’t necessarily preclude ‘doing science’ – it could just mean that the pressure to publish is reduced. However, it might also mean that you have less freedom over the things that you do.
  • Consider approaching an organisation that isn’t a university. Kew Gardens, or the National Biodiversity Network, for example.
  • Do think about career alternatives – decide what you’d be trying to do if you weren’t considering an academic job, and start acquiring the skills that you’d need if you were to apply for a role in that field.
  • One particularly focused way of obtaining the skills you might need is to volunteer. Personally, I’m interested in science communication – I’ve managed to develop some of the relevant skills through maintaining this blog, along with others, maintaining social media profiles for the organisations that I’ve worked with, and maintaining websites. You may be able to pick up some of the skills you need in a way that enhances your current role.
  • The ScienceCareers Individual Development Plan site might be work a try.
  • There are some useful materials on Versatile PhD.
  • Dynamic Ecology has some interesting perspectives on the matter:

* It stands for ‘Networks in Ecology/Evolution Research Dynamic
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Cooking with soil

Beardyman knows how to combine the various beats in his kitchen to produce something delectable. The culinary analogy extends to analysing soil in the laboratory too and, while unlikely to grace the pages of the Sunday supplements, thinking about some complicated labwork in terms similar to baking a cake is a good way of making it less daunting – like baking, you need to be precise with your timings and temperatures, and not mix things that aren’t supposed to be mixed. Easy! Below is a recipe for a analysing the microbial community in a peaty soil (using a method called multiplex-Terminal Restriction Fragment Length Polymorphism, or m-TRFLP, but you didn’t really need to know that…). Don’t try this at home, obviously.

You will need:

  • hexadecyltriammonium bromide in salty water
  • polyethylene glycol in salty water
  • phenol
  • chloroform:isoamylalcohol
  • 70% ethanol
  • some very expensive primers
  • some restriction enzymes
  • a lysing machine
  • a centrifuge
  • a thermal cycler (PCR machine)
  • a PCR clean-up kit (I prefer Wizard)
  • a sequencer
  • plenty of ice
  • lots of gloves
  • DNA remover
  • a UV cross-linker for destroying DNA on tubes
  • lots of little tubes
  • some soil (finely chopped, frozen)
  • a couple of days
  • reserves of patience

Method:

  1. Measure out 0.5 g of soil, and add to a lysing tube with some phenol, hexadecyltriammonium bromide solution and chloroform:isoamylalcohol. Do this in a fume cabinet, otherwise things will go terribly wrong. Stir.
  2. Shake the mixture for about 30 seconds at 5000 meters per second in your lysing machine. Ensure that your lids are securely fastened to your tubes, otherwise your soily chloroform-phenol mixture will spray itself all over the inside of your lysing machine, and you’ll have to spend ages cleaning the machine while holding your breath.
  3. Put your mixture on ice and take it to the centrifuge. Once again, ensure that your lids are securely fastened. Whizz for ten minutes at 10,000 rpm.
  4. Carefully take your mixture out of the centrifuge and transfer the top, clear layer to a clean tube. Avoid hoovering up any of the gooey soily mix with your pipette. Add more chloroform:isoamylalcohol to the clear liquid and return to the centrifuge for five more minutes. Don’t forget to autoclave your hazardous waste!
  5. Transfer the top layer once more into a clear tube, and add some polyethylene glycol solution. Stir well, and leave on ice (not in the fridge, where someone might mistake your tubes for free samples of some quirky new energy shot product) for a couple of hours.
  6. Put your tubes back in the centrifuge at 10,000 rpm for ten minutes. You should now have an almost-invisible pellet of DNA at the bottom of the tube. A steady hand is required for the next step, so go easy on the coffee.
  7. Carefully remove the liquid from your tube, replace with ice-cold 70% ethanol, and centrifuge again for five minutes. Don’t be tempted to sample the ethanol.
  8. Remove the ethanol from your tube, leaving your DNA pellets in a warm, dry place for about half an hour. Once dry, add a drop of autoclaved water to your tube, and place in the fridge.
  9. At this point, it’s a good idea to check the concentration of DNA in your samples, to see whether your extraction has worked. Ask a grown-up to do this for you.
  10. Pre-program your thermal cycler. Add your choice of primers to your DNA, reading the instructions carefully. Stir well. Put your tubes in the thermal cycler for about three hours. If, at this stage, you’re exhausted, you can leave your tubes in the thermal cycler overnight, as long as you’re not planning a lie-in the following morning.
  11. Use a Wizard (kit) to clean-up your newly-amplified DNA. Pre-program your thermal cycler again, ready for the restriction digest.
  12. Add your restriction enzyme of choice (I like Hha1) to your cleaned-up DNA. Place in the thermal cycler for about one and a half hours.
  13. Check the final concentration of your DNA (ask a grown-up to help you with this). If you haven’t booked a slot on the sequencer, now is the time to go pleading to your lab manager.
  14. Place your tubes in the sequencer, overnight. In the morning, serve your data with some multivariate stats and a tentative garnish of interpretation. Delicious!
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R graphics: varying geoms between facets in ggplot2

Hadley Wickham’s ggplot2 package is a very powerful and (once you’ve got used to it) intuitive R graphics framework, based on the Grammar of Graphics, that most R users will come across at some point. One of its most useful features is facetting: splitting data up between multiple plots, in the same window (or device), based on some aspect of the data – usually a factor.

Facetting is great but, as with many aspects of ggplot2, it lacks some flexibility. The reasons for this lack of flexibility are often sensible, but it can be frustrating. We can use facets to split data, but there’s no option to vary the geoms used to plot data between facets: all facets must share the same geom, e.g. bars, or lines. This can be frustrating when some aspects of your data would be better represented using a different geom to the one you’ve already chosen.

Yesterday, however, I came across a work-around for this via this Stat Bandit blog post. The work-around uses the subset() function within each geom to control which facet each geom in plotted on. I’ve included an example below, which illustrates plotting monthly counts of blog views alongside a cumulative count. My code is based on that on the aforementioned post, so do check that out too.

Blog views

# Load libraries
library(ggplot2)
library(reshape)

# Load data
BLOGVIEWS = read.table("blogviews.txt",
						header = T,
						sep = "\t")

# We have times series data, with one observation per month
# Convert into Date class, specifying "1" as the day of the month						
BLOGVIEWS$DATE = as.Date(paste("1", BLOGVIEWS$MON, BLOGVIEWS$YEAR),
						format = "%d %b %Y")

# Check that our data look as we expect						
str(BLOGVIEWS)

# We want to replace NAs (representing zero views) with 0
BLOGVIEWS$VIEWS[is.na(BLOGVIEWS$VIEWS)] = 0
# Next we calculate cumulative site views by month
BLOGVIEWS$CVIEWS = cumsum(BLOGVIEWS$VIEWS)
# Check the results
BLOGVIEWS

# To plot the data using facets, we need to reshape the
# data into 'long' format using melt
(BVIEWS.MELT = melt(BLOGVIEWS, id.vars = c("DATE", "MON", "YEAR")))

# Change the levels of the 'variable' factor so that our
# facets have sensible names
levels(BVIEWS.MELT$variable) = c("Monthly views", "Cumulative views")

# The first plot sets up the axes and facets, but we
# use geom_blank to draw a blank plot, which we'll add
# geoms to next
g1 = ggplot(BVIEWS.MELT, aes(DATE, value)) +
		facet_wrap(~ variable,
					nrow = 2,
					scales = "free_y") +
		labs(x = "Year", y = "Number of views")

# Update the first plot, adding bars to display monthly counts
# The subset operation ensures that we only add to the facet
# corresponding to 'Monthly views'
g2 = g1 + geom_bar(subset = .(variable == "Monthly views"),
					stat = "identity")
# Do the same for the 'Cumulative views' facet. It makes
# more sense to display these data using geom_line					
g3 = g2 + geom_line(subset = .(variable == "Cumulative views"),
					colour = "blue",
					size = 1)
# Finally, print the plot and save it to a .png image file					
print(g3)
ggsave(file = "g3.png")
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The Paper Parade: my Top Ten

The British Ecological Society recently published a list of 100 influential papers, written over the last 100 years, as part of its centenary celebrations. I like zeitgeisty things like this, and enjoyed perusing the papers, each reference accompanied by a write-up by a scientist-in-the-know. It prompted me to think: which papers would be in my personal top ten? They could be papers that inspired me to take a certain approach, to consider my research in a different way, or papers that I just enjoyed reading because I thought they (or the authors) were cool.

What do you think? Do you have any papers that grabbed you by the proverbial lapels and never let go? Post your suggestions in the comments below!

Here’s my top ten (controversially, in no particular order):

Franklin, J. (1995) Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, 474–499.

I first came across this paper during my undergraduate degree studies at the University of Southampton. It inspired me to develop my interest in (plant) species distribution modelling, the topic I ended up focusing on for my dissertation. In her review, Janet Franklin defined predictive vegetation mapping, outlined major contributions to the topic, provided a conceptual framework, and discussed approaches and methods (such as deriving variables from digital elevation models) in detail. The field of (plant) species distribution has advanced a long way since 1995, in tandem with the availability and sophistication of high-resolution spatial data, computing power and statistical methods. However, Janet Franklin’s review has stood the test of time, as far as providing an accessible introduction to the area of species distribution modelling is concerned.

Beilman, D.W., Vitt, D.H., Bhatti, J.S. & Forest, S. (2008) Peat carbon stocks in the southern Mackenzie River Basin: uncertainties revealed in a high-resolution case study. Global Change Biology, 14, 1221–1232.

My PhD supervisor, Nick Ostle, introduced me to this paper early in the first year of my PhD. David Beilman and colleagues used the Mackenzie River Basin in Canada as a case study for getting better estimates of the amount of carbon stored in peatlands, using a combination of landcover data, digital terrain data and peat carbon and depth data collected from other studies to produce a map of carbon stored per unit area of peatland. The study emphasised the importance of generating accurate peatland carbon inventories, and the approach adopted by David informed my own sampling strategy and later spatial analyses as I worked to produce a similar map of carbon storage for my PhD study site.

Couwenberg, J., Thiele, A., Tanneberger, F., Augustin, J., Bärisch, S., Dubovik, D., Liashchynskaya, N., Michaelis, D., Minke, M., Skuratovich, A. & Joosten, H. (2011) Assessing greenhouse gas emissions from peatlands using vegetation as a proxy. Hydrobiologia, 674, 67–89.

John Couwenberg and colleagues applied the very neat and convenient concept of using vegetation composition as a proxy for measured greenhouse gas emissions, testing the concept at two Belarusian peatlands. The idea that vegetation is a suitable proxy for greenhouse gas emissions has its origins in the fact that, in peatlands, water table level is an important driver of greenhouse gas emissions. The authors assert that vegetation is a good indicator of water table level and other factors that determine the amount of greenhouse gases emitted from peatlands. Vegetation patches are easy to map, and represent the combination of multiple years of conditions contributing to greenhouse gas emissions. Associating peatland greenhouse gas emissions to vegetation in this way allows us to test scenarios of vegetation change in an intuitive manner – an idea that I was keen to embrace in my own PhD project using a coarser landform approach, which I think will be useful for estimating greenhouse gas fluxes from peatlands using aerial photographs.

McNamara, N.P., Plant, T., Oakley, S., Ward, S.E., Wood, C. & Ostle, N. (2008) Gully hotspot contribution to landscape methane (CH4) and carbon dioxide (CO2) fluxes in a northern peatland. Science of The Total Environment, 404, 354–360.

This paper from Niall McNamara and colleagues at the Centre for Ecology and Hydrology is another example of a paper that I read early on in my PhD, which shaped my approach from the beginning. In this study, Niall and colleagues measured methane emissions from gullies and surrounding open moorland at what was to become my PhD study site, a blanket peatland in the north Pennines, England. They found that the gullies had much higher methane emissions than the surrounding bog and, despite covering just 9.3% of the land area, accounted for 95.8% of the methane emissions from the entire blanket peatland. The study confirmed that status of wet, Sphagnum- and sedge-filled gullies as hotspots for methane emissions. For me, this emphasised the importance of finding reliable estimates of greenhouse gas emissions from these landforms at the landscape scale – and why not apply the same idea to eroding areas as well? In my PhD, I found that these patches of bare peat played a similar role to gullies, in accounting for a disproportionately large amount of greenhouse gas emissions at the landscape scale, given their relatively small area.

Mitchell, R., Hester, A., Campbell, C., Chapman, S., Cameron, C., Hewison, R. & Potts, J. (2010) Is vegetation composition or soil chemistry the best predictor of the soil microbial community? Plant and Soil, 333, 417–430.

This paper by Ruth Mitchell and colleagues at the James Hutton Institute is one of my favourites, because it features a clever statistical method called Co-Correspondence Analysis, which can be used to determine how useful one community (the plant community, for instance) is for predicting another community (how about the soil microbial community immediately beneath those plants). This is another paper on the theme of using what we can easily see and survey (the plant cover) to predict what we can’t see as easily (the soil microbes, or the greenhouse gas emissions). Ruth found that the plant community predicted the soil microbial community just as well as the soil chemistry. I was keen to see if the same could be said of the plant communities present at my PhD study site, so tried it myself (unfortunately, with less convincing results!).

Treseder, K.K., Balser, T.C., Bradford, M. a., Brodie, E.L., Dubinsky, E. a., Eviner, V.T., Hofmockel, K.S., Lennon, J.T., Levine, U.Y., MacGregor, B.J., Pett-Ridge, J. & Waldrop, M.P. (2012) Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry, 109, 7–18.

I’ve chosen this paper as one of my favourites because it introduced an idea that I think is particularly important to consider when we’re thinking about modelling the responses of ecosystems to global change pressures, like nitrogen deposition, or a warming climate. Kathleen Treseder and co-authors make the point that integrating some knowledge of soil microbial communities in our models will probably improve the performance of those models. As far as my own research is concerned, this paper helped me to justify the choices I made when selecting the scales at which to measure the soil microbial communities at my own PhD study site – I wanted to characterise them in a way that would perhaps be useful for future use in models of peatland carbon and nitrogen cycling.

Van der Heijden, M.G.A., Bardgett, R.D. & van Straalen, N.M. (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters, 11, 296–310.

This review paper by Marcel Van Der Heijden and co-authors caught my attention in the first year of my PhD thanks to that titular concept: soil microbes are the unseen majority, like the stage crew working behind the scenes at a West End show that makes the ‘magic’ happen. Well, sort of. The idea of soil microbes as drivers of the patterns we see in aboveground plant communities is one of several theoretical jumping-off points I adopted when drafting my literature review and rationale for my PhD studies. That image of the ‘unseen majority’ captured my imagination.

Wardle, D.A., Bardgett, R.D., Klironomos, J.N., Setälä, H., Putten, W.H. van der & Wall, D.H. (2004) Ecological linkages between aboveground and belowground biota. Science, 304, 1629–1633.

I consider this work by David Wardle and co-authors to be one of the definitive above-belowground interactions papers. The review sets out the concept of linkages between aboveground and belowground communities, and goes on to describe how these linkages can affect the functioning of ecosystems, before suggesting some research priorities. It also emphasises that these interactions are likely to be dependent on spatial and temporal context, and that this should be taken into account when designing studies that focus on this area. While I haven’t focused explicitly on this area in my own research, it’s certainly provided me with plenty of interesting background reading that’s helped to inform my ideas about the interactions between plants and microbes at different scales.

Bardgett, R.D., Freeman, C. & Ostle, N.J. (2008) Microbial contributions to climate change through carbon-cycle feedbacks. The ISME Journal, 2, 805–814.

This mini-review from Bardgett, Freeman and Ostle represents another early-PhD blockbuster, which provided me with a concise picture of the conceptual framework through which microbes mediate the carbon cycle and associated feedbacks with the climate. Figure 1 has appeared – sometimes in different guises – many times in various talks and presentations I’ve attended throughout my PhD and afterwards, which demonstrates the paper’s continuing relevance.

Lindo, Z. & Gonzalez, A. (2010) The Bryosphere: An integral and influential component of the Earth’s biosphere. Ecosystems, 13, 612–627.

And finally, last but by no means least, Zoe Lindo and Andrew Gonzalez present the wittily-named Bryosphere in a mini-review, in which they discussed the value of globally ubiquitous mosses as components in carbon and nitrogen cycling, and The Bryosphere as an ecosystem in itself, often under-valued. I’ll admit to being hooked by the title before the content, but it’s good, isn’t it. Rather than summarise it here, I’ll suggest that you read the article for yourself, and then go outside and appreciate The Bryosphere, in all of its glory.

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Bog standard: Doing a PhD about peat (or anything else for that matter) – part 2


Here’s part 2 of my plethora of peaty PhD pointers. Please feel free to leave comments about your own experiences!

  1. Distractions
    1. Friends
    2. The Blogosphere, Twitface and KittehCam
  2. Here be dragons. Statistical dragons
    1. Rarr. Rarrrr! Arr. R.
    2. Hug a statistician
    3. What are you doing again?
    4. Graph it
    5. Stopping
  3. Writing and talking
    1. Referencing software
    2. Write your thesis in 6 short weeks with this amazing tip!
    3. Zen
    4. Imagine them in their underwear
  4. Getting it together
    1. Word is not your friend
    2. All styles, and substance
    3. Graphs and images
    4. Fields of pain
    5. Keeping it together
  5. The End
    1. What happens at the end
    2. The V-word
    3. After the end

4. Distractions

Friends

I’ve been lucky to have been included in three different research groups, with fantastic people in each. My friends have provided a venting mechanism, sounding board, debating forum, vast amounts of good advice, and – most importantly of all – a jolly good time. Get to know people when you start your PhD and keep on making friends throughout, get out there and join in – you never know when you might need them! My experience has been made richer by the people I’ve been surrounded by.

The Blogosphere, Twitface and KittehCam

If you’re reading this, well, excellent. But if you’re in the middle of a PhD, perhaps you should stop here and save the rest for your lunch break! Procrastination is an art easily mastered, but apparently never perfected: you just have to keep going back for more practice. Access to high-speed internet is both your best friend (papers! access to distributed computing! downloadable software!) and your worst enemy (long reads on The Guardian! KittenCam! Wimbledon!). It’s good to have a break, and the consequences of getting entirely distracted are less severe at the beginning of a project, but like anything else, time becomes more valuable as it diminishes. To get my PhD finished on time, I had to be very strict with myself, actually signing off Facebook and Twitter for two months so that I wasn’t tempted to procrastinate. There are services you can sign up to that will restrict your access to distracting websites for you, but why bother? I found the threat of not actually being finished on time was enough!

The lure of doughnuts was too much for the author.

The lure of doughnuts was too much for the author.

5. Here be dragons. Statistical dragons.

Rarr. Rarrrr! Arr. R.

If you’re doing a PhD in any sort of quantitative discipline, chances are that you will come across R at some point. Whispered along the corridors of educational institutions, and enough to prompt feelings of fear and uncertainty in the uninitiated, the 18th letter of the alphabet has become infamous among PhD students all over the world. Actually, it’s not that bad. In fact, it’s really great. R is a stats package and a programming language, which has several advantages over the competition:

  • It’s free
  • It has an ever-expanding library of functions and help manuals written by users, for users
  • It’s infinitely flexible
  • You get kudos for using it
  • You get to pretend you’re a pirate

There are two disadvantages. Firstly, because R has a command-line interface, you can’t just point-and-click your way through functions, resulting in a steep learning curve. Secondly, when you do turn to the help, or online messaging boards, for assistance, you’ll probably find it rather hostile and obtuse. But don’t be put off – there are lots of great, accessible books on R out there for all disciplines, and so many people use it, there’s bound to be a helpful PhD student or post-doc you can turn to for help.

Hug A Statistician

You might be lucky enough to know precisely what you want to do with your data, once you’ve collected it, but for most of us, choosing the best method for testing a hypothesis can be a tricky problem. This is where your institute’s statistician comes in. I found that the discussions I had with the statistician at my institite were helpful, not condescending, and versed in a way that was simple enough for me to go away and process the information into a decision. Not every institute will have such an approachable statistician, but the maths department might offer consultancy as a service to PhD students, or there’s your fellow students and post-docs. Many minds can often be better than one.

What are you doing again?

Once you’ve got your head around the statistical wizardry, it can be easy to plough ahead and start creating a host of complicated models. At this stage, I found it helped to return to my original questions. What questions was I trying to answer? Which techniques would be best for answering them? Once I had these ideas settled in my head, I drew a flowchart, starting with the data, stating the question, and finishing with the desired method I would use to answer it (usually an R function). The more specific you can be, the easier it will be to keep track of what you’re doing, and what you’ve already done – if you end up repeating tests without realising you’d already done them a couple of days ago, you certainly won’t be alone! In this respect, it’s a good idea to keep a journal for your stats, in the same way as you’d keep a lab book for your work in the lab.

Too much time doing stats can do funny things to some people.

Too much time doing stats can do funny things to some people.

Graph it

Drawing plenty of graphs, at each stage of your analysis, will help you to interpret your results in the context of the real world, and discuss what your data are really showing, rather than confusing your audience with coefficients (while necessary, these aren’t the most reader-friendly way of communicating your results).

Stopping

Remember when you were doing your fieldwork, and you decided to stop because you had enough data to answer the questions you’d set yourself? The same approach can be applied to your statistics. Once you’ve answered your questions using your chosen method, and your happy that your results are robust, don’t go back and try another couple of different methods, just for fun and because you want to flex your new-found statistical muscle. There be dragons! And much wasted time. Stick to the plan, otherwise you could be there forever.

6. Writing and talking

Referencing software

This is important, and worth getting right before you start. Knowing your way around a referencing software package will make your life many orders of magnitude easier when it comes to writing up: a good package will, as a minimum, maintain a database of papers you’ve read, insert references into your documents and compile a bibliography in your chosen style. This is neither the time, nor the place, for an exhaustive review of different packages – among the most well-known are EndNote, Papers, Mendeley and Zotero. I prefer Mendeley personally – it’s free, comes with plenty of cloud storage for pdfs (which you can upgrade for a fee), and is easy to use. Whichever package you decide to adopt, it’s worth learning how to use it properly before you get elbow-deep in writing, then sticking with it until the end, because you won’t want to be working out how to transfer databases between packages with deadlines to meet!

Write your thesis in 6 short weeks with this amazing tip: the ‘chapter a week’ programme

So you’ve spent many backbreaking hours in the field and lab, followed by numerous late nights in front of your favourite statistical package, analysing those hard-won results. Now the time has come to put all that work into words. When I was finally ready for this, time was short, so my supervisor suggested a radical new programme: I would write a chapter in a week. Easy!

Monday: Monday is methods day. The methods are the easiest place to start. Write down what you did, and while you’re at it, write an abstract: what was the worldly problem, what questions did you ask, what were the answers and the implications?

Tuesday: Results text, tables and graphs. Be prepared to cut your graph-tweaking time short – there’ll be time for polishing later.

Wednesday: Back to the introduction, which is composed of five bits: background, knowledge gap, questions, objectives and hypotheses.

Thursday: Four days into the week, you should still be feeling fresh, and a good thing too, for today is discussion day. Put those findings in context! Don’t forget to write a conclusion and discuss the implications of your work.

Friday: Go back to the start and review the whole lot. 5pm is pub o’clock.

Hiding from your PhD won't help.

Hiding from your PhD won’t help.

Zen

Giving a talk before an audience is probably one of the most dreaded aspects of the research experience – I know plenty of postdocs who still get sweaty palms over it, despite years of experience. When I embarked upon my PhD, I knew that this was something I wasn’t terribly comfortable with, so I decided to make the best of it, rather than avoiding the issue. One of the best tips I can give is: be prepared. In reality, this is sometimes difficult, but it’s worth getting your talk ready well in advance and practicing it in front of a substitute audience. This will give you an opportunity to check that you run to time, and take some constructive criticism from colleagues. The less you leave to chance, the less nervous you’re likely to be when the time comes to face your audience for real.

Imagine them in their underwear

Of course, standing up and talking about your work can be very nerve-wracking. There are three coping mechanisms that tend to work for me (the above isn’t one of them). Firstly, do something you enjoy beforehand, to put your mind at ease, and stop you worrying too much. Secondly, be confident. Or at least appear confident – you may well be quaking inside, but once you get through the first couple of slides with an air of authority, you’ll have your audience’s attention and will have generated some momentum to carry you through the next slides. Thirdly, smile and try to enjoy the experience – you deserve to be there, communicating your work.

7. Getting it together

Word is not your friend

It’s probably a given that most people will, at some point, fall foul of Microsoft Word’s irritating and often seemingly random formatting system. Since this is the system that most people (aside from those brave souls who have embraced LaTEX – all power to them, too) will use to write up their theses, I’ve taken some time to outline a few tips that might help to save you from text-based strife.

See this chap: ¶ – clicking the Pilcrow symbol on the Word toolbar will toggle formatting marks on and off. Some might find that leaving it switched on makes the screen a bit too crowded, but I find it useful for understanding the mechanics at work inside a document. It’s particularly useful when you’re working with page, section and column breaks, which can all be employed to good effect to reign your text in and keep it where you want it.

All styles, and substance

Styles in Word can be used to automate the process of setting up a table of contents, which will be much appreciated when you’re at the end of the writing process and just want to get shot of the thing. Headings one to five can be used to create indented headings in the table of contents, which can be automatically updated as you edit your document. This is one of those labour-saving devices that I found really useful.

Graphs and images

Inserting images in Word documents is always a bit of an unpredictable affair. I like to try and hang on until I’ve finished writing the main text before I go ahead and insert the images where I’d like them to appear in the final document – if you’re still moving large chunks of text around, you can put money on the likelihood of Word doing something absurd with the text wrapping or anchor placement, leaving your image several pages from where it should be. Once you’re happy with the text, switch on those formatting marks (¶) and create spaces where you’d like your images to sit – usually just one line is enough. With the cursor where you want the image to be, press the insert button, try not to panic about what might have just happened, and set the text wrapping to ‘in-line’ with text. I do things this way for two reasons: firstly, you know where you are, and your image won’t wander the pages unduly; secondly, in order to speed up scrolling through your document once you’ve accumulated several images, you can tell Word to just display the placeholders (Options | Advanced > Show document content) – this option only works with in-line images.

Formatting turned out to be a tricky affair.

Formatting turned out to be a tricky affair.

Fields of pain

Reference software is jolly useful, but can have one drawback connected with the creation of the table of contents, or tables of figures. Because reference software often uses active fields to link to the reference database – these show up with a grey background when you click on them in your document – they can conflict with the table of contents, which also uses active fields. To avoid any unseemly conflicts, once I’d finished inserting all my references and the bibliography, and was happy with them, I used the option to export the document without active fields to create a ‘clean’ copy – all the references and bibliography are exported as plain text. Most reference packages will have this option, and it’s worth using if you want to avoid potentially confusing conflicts between field codes. Something to bear in mind, however, is that once you’ve exported the document without the field codes, there’s no longer any link to the reference database, so you’ll have to make further changes manually.

Keeping it together

At this stage, you’ll probably be quite good at keeping it together emotionally. After all that time spent writing those precious words, it’s vital to be organised and keep your stuff together physically, too. At the very least, keeping your files backed up will save you having to do everything all over again. This is where cloud storage services like Dropbox, Google Drive, SpiderOak, Skydrive, etc., come into their own. As long as you have an internet connection, you can synchronise a folder on your computer at university, continue to work on your documents, then go home, synchronise the folder on your laptop, and work some more. These services aren’t infallible, but they’re much more robust and less easy to loose than portable hard drives and USB sticks. For those with tech-savvy supervisors, you can use shared folders to send your work to supervisors as soon as you’re ready, which avoids those awkward missed emails and filled inboxes.

The author, multitasking.

The author, multitasking.

8. The End

What happens at the end

Congratulations – you’ve made it! Three / four years’ work, all summed up in an epic thesis. Now all there is to it is to print the thing, get it bound, and hand it in. At this point, if you haven’t already (in which case, you do like cutting it fine, don’t you!) a thorough read of your institute’s guidelines on thesis submission comes highly recommended. Even if you’re really up against it, keeping a couple of days free to get your thesis printed and bound will avert any last-minute crises. You may well be stressed and sleep-deprived, so getting your friends involved – particularly when out-sourcing your proof-reading – is a good idea!

The V-word

After all the parties and holidays have dwindled and your life has returned to (an albeit empty-feeling) state of relative normality, it’s not a bad idea to start thinking about your viva. Picking up the thesis and reading it through can be a dispiriting exercise, especially if you had as many spelling mistakes as I did, but it’s absolutely necessary. Pre-viva nerves can be effectively mitigated by knowing the answers to some simple (!) questions:

  • What did I set out to find out / achieve?
  • How did I structure my project, and why?
  • Why did I use those methods? This is a big one: you have to show that you’ve understood the limitations of the methods you used, for data collection and analysis. You’ll need a scientific basis for all the decisions you made – ‘it was what everyone else did’ won’t cut it with your examiners.
  • What did I find?
  • How do my findings fit in the context of other work?
  • What would I do differently?

Like the PhD itself, viva examinations are very personal, and it’s quite likely that someone else will come up with a different set of questions to those above. Asking around can be another good coping strategy for pre-viva nerves.

After the end

What I’ve written about here is based on my experiences of a very personal thing – the PhD – and I’m sure that not everyone will agree with the points I’ve made above. But one of the aspects of being a postgrad that I’ve enjoyed the most is being able to share experiences with other students in a similar position. If you have points and comments of your own to add, please do add them below!

It was worth it in the end.

It was worth it in the end.

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Bog standard: doing a PhD about peat (or anything else, for that matter), part 1


Since finishing my PhD, I’ve felt the need to share my experiences: the great wealth of hints and tips that I was lucky to glean from friends and mentors, as well as that most helpful and elusive of things – hindsight. PhDs are intensely and uniquely personal experiences, and this series of experiences, hints and tips could only ever be a personal account. Nevertheless, I hope it makes amusing reading at the very least, and perhaps it might even be of use to you!

  1. Beginnings
    1. So what are you doing?
    2. The Temple of All Knowing
  2. On fieldwork
    1. Forgetting
    2. Slaves
    3. Let’s be sensible
  3. In the laboratory
    1. Hours. And hours and hours and hours…
    2. Rise of the Machines
    3. Take a break!

1. Beginnings

So what are you doing?

Starting a PhD certainly shouldn’t be a decision that is taken lightly – you’re about to embark on a three/four/five year odyssey of scientific endeavour, a quest for knowledge that will take you to the very edge, and back again, probably several times. Battles will be fought and lost, but victory will also be yours. Some things will have to be sacrificed. You might cry. But you will also laugh, and probably get drunk. On Fridays, you will do both. Without becoming further tangled in the brambly bush of tangents, suffice to say, you’d better like the topic you decide to study, or at the very least find it interesting. Otherwise boredom might quickly turn to resentment and loathing.

Having said that, the decision to begin a PhD is rarely as simple as deciding whether or not you’re into the research topic. I ended up stumbling into mine, as it was suggested at the back end of a job interview. Neither me, nor my potential supervisors, could have any idea of the true nature of the perilous voyage ahead, but I was quite into plants and soil, and liked the sound of carbon cycling, and three years of funded research (i.e. a job) beckoned, so I was in.

The Temple of All Knowing

Beginning a PhD can be a bewildering experience – there are many new people to meet, IT and lab technicians to get friendly with, maybe other supervisors to meet, and stipends to chase. Before getting too involved, it’s really worth getting to know the library (at each of your institutions, if there are more than one): how many books you can get out, how to access their journal collections, how to get access from home using VPN or a password, how to get inter-library loans organised, or request new material. Knowing your stuff about the library will save you time when it comes to writing your literature review, and make your job much easier. They might also offer courses on how to search databases effectively, or use reference manager software (more on that later).

2. On Fieldwork

Forgetting

Picture the scene. After walking a mile across bumpy ground, through knee-high shrubs, following a two-and-a-half hour drive, I stop, put down my box (heavy with expensive kit) and – relieved – take off my rucksack. The relief, however, is short-lived. Rummaging through my box of sampling equipment, I can’t find four unassuming black modules, which are vital components of the sampling that is the sole purpose of my trip. Without them, I can’t do anything. The unthinkable flashes through my mind as I throw items of clothing from my rucksack. I pick up my phone. At first, the report from my friend is positive: he can’t find anything small and black on my desk – oh, wait, might these small black modules be the ones I mean?

I hang up. All I will have achieved is five hours of driving, and half an hour of yomping over rough ground: a waste of half a tank of diesel and most of a day. This was by far the worst experience of forgetting I had while on fieldwork, but by no means the only one. After that, I made a comprehensive list of things I’d need for each sampling trip, and ticked it off rigorously each time I loaded up the pickup to go sampling again.

The author gets his priorities right.

The author gets his priorities right.

Slaves

Or volunteers, as I believe they like to be called. Unless you’re really efficient and have sampling that is achievable in a human lifetime with two hands, you’ll need some help at some point. I’ve been lucky to work in some great groups, where the help has been readily available on the condition that the favour is returned in kind in the future. This system works really well. Apart from the promise of favours returned, cake, sweets and beer are also excellent incentives. Remember to keep your slaves volunteers well-fed, watered and entertained, and they’ll probably come again.

Let’s be sensible

There comes a time in any project where you have to take a step back to consider the bigger picture, particularly concerning how much time you have left and the questions you still want to address. Fieldwork is demanding, expensive, and exhausting if you’re doing it right, so it’s worth regularly considering whether you’re collecting the most appropriate data. Collaborating with others is a great way of sharing the load and is beneficial for both parties. Don’t just continue fieldwork for the sake of it – if it’s not addressing one of your questions, you’d be better off going back to the drawing board and doing something that does.

'adversity'

‘adversity’

3. In the laboratory

Hours. And hours and hours and hours…

The only thing that takes nearly as much time as fieldwork is labwork. It can take absolutely ages, so you should be prepared for much tedium. Investment in some form of portable music device, or even a pair of speakers for the PC in the lab, will stop you from going mad, or at least delay the process. Another good way of saving time is to keep a really thorough notebook – if I had done this right from the beginning, I would have spent much less time hunting for samples I’d diligently hidden deep in freezers. Write everything down, because you never know what might happen.

So many samples...

So many samples…

Rise of the Machines

It’s worth becoming familiar with the instruments (in a purely professional manner, obviously) before it’s your turn to use them, if you can, perhaps by offering to run samples for other people when they are away, in exchange for some tuition. This is where all those hours invested befriending the lab technician will come in useful. If it’s not clear exactly how an instrument is supposed to be used or maintained, or how many standards should be included in a run, it’s worth finding out before starting to analyse those precious samples. If there isn’t a protocol, write one – it’ll be appreciated eventually, and you’ll bask in the glory of being the go-to expert for that machine.

It's a good idea to get stuck into lab duties.

It’s a good idea to get stuck into lab duties.

Take a break!

All those hours spend sieving, drying, weighing, pipetting and extracting can take their toll on the soul, so it’s a good idea to structure your lab work so that you can take breaks to regain your sanity and remind yourself what people look like. This generally involves not leaving your lab work until the last minute, so that you don’t have any choice but to plough on through into the early hours. Setting yourself regular breaks during the day for coffee / email pit-stops will act as an incentive, and the less tired you are, the more effective you’ll be, and the fewer mistakes you’re likely to make.

Continue to part 2
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