The current PhD travel budget of £2000 doesn’t go far, especially if you have your eye on attending the AGU Fall Meeting in San Francisco. If the world ever goes back to normal (and fingers crossed it will – though hopefully with more greener travel options, and remote participation in shorter conferences?) you might wonder how you are ever going to afford the conferences your supervisors suggest. Luckily, there are many ways you can supplement your budget. Receiving travel grants not only means more conferences (and more travel!), but it also looks great on your CV. In this blog post I share what I have learnt about applying for conference grants and list the main places to apply.
Unlimited payments until you have received £300 in total
4 deadlines throughout the year: 1st March, 1st June, 1st September and 1st December
Note: you apply for funding from an IOP group, and the conference must be relevant to the group. For example, most meteorology PhD students would apply for conference funding from the Environmental Physics group. You get to choose which groups to join when you become an IOP member.
2 deadlines each year: 15th February and 15th August
From the conference organiser
Finally, many conferences offer their own student support, so it’s always worth checking the conference website to see
Both EGU and AGU offer grants to attend their meetings each year
Many of these schemes take months to let you know whether you have been successful. Becoming a member can also take a while, especially when societies only approve new members at certain times of the year. So, it’s good to talk to your supervisor and make a conference plan early on in your PhD, so you know when to apply.
Writing your application
Generally, these organisations are keen to give away their funds, you just have to write a good enough application. Keep it simple and short: remember the person reading the application is very unlikely to be an expert in your research. It can be helpful to ask someone who isn’t a scientist (or doesn’t know your work well) to read it and highlight anything that doesn’t make sense to them.
Estimating your conference expenses
You are usually expected to provide a breakdown of the conference costs with every application. The main costs to account for are:
Accommodation: for non-UK stays must apply for a quote through the university travel agent
Travel: UK train tickets over £100 and all international travel must be booked by university too
Subsistence: i.e. food! University rules used to say this could be a maximum of £30 per day – check current guidelines
Conference Fees: the conference website will usually list this
The total cost will depend on where the conference is. You are generally expected to choose cheaper options, but there is some flexibility. As a rough guide: a 4-day conference within the UK cost me around £400 (in 2019) and a 5-night stay in San Francisco to attend AGU cost me around £2200 (in 2019).
Just over a month ago in September 2020, I started my journey as a PhD student. Since then, have I spent all of my working hours solely on research – plotting radar scans of heavy rainfall events and coding up algorithms to analyse the evolution of convective cells? Surely not! Outside my research work, I have also taken on the role of demonstrating this academic year.
What is demonstrating? In the department, PhD students can sign up to facilitate the running of tutorials and problems, synoptic, instrument, and computing laboratory classes. Equipped with a background in Physics and having taken modules as an MSc student at the department in the previous academic year, I signed up to run problem classes for this year’s Atmospheric Physics MSc module.
I have observed quite a few lectures during my undergraduate education at Cambridge, MSc programme at Reading and also a few Massive Open Online Courses (MOOCs) as a student. Each had their unique mode of teaching. At Cambridge, equations were often presented on a physical blackboard in lectures, with problem sheet questions handed in 24 hours before each weekly one-hour “supervision” session as formative assessment. At Reading, there have been less students in each lecture, accompanied by problem classes that are longer and more relaxed, allowing for more informal discussion on problem sheet questions between students. These different forms of teaching were engaging to me in their own ways. I have also given a mix of good and not-as-good tutorial sessions for Year 7s to 13s. Good tutorials included interactive demonstrations, such as exploring parametric equations on an online graphing calculator, whereas the not-as-good ones had content that were pitched at too high of a level. Based on these experiences and having demonstrated for 10 hours, I hopefully can share some tips on demonstrating through describing what one would call a “typical” 9am Atmospheric Physics virtual problems class.
PhD Demonstrating 101
You, a PhD student, have just been allocated the role as demonstrator on Campus Jobs and are excited about the £14.83 per hour pay. With the first problems class happening in just a week’s time, you start thinking about tools you will need to give these MSc students the best learning experience. A pencil, paper, calculator and that handy Thermal Physics of the Atmosphere textbook would certainly suffice for face-to-face classes. The only difference this year: You will be running virtual classes! This means that moist-adiabatic lapse rate equation you have quickly scribbled down on paper may not show well on a pixelated video call due to a “poor (connection) experience” from Blackboard. How are you going to prevent this familiar situation from happening?
Figure 1: Laptop with an iPad with a virtual whiteboard for illustrating diagrams and equations to be shown on Blackboard Collaborate.
In my toolbox, I have an iPad and an Apple pencil for me to draw diagrams and write equations. The laptop’s screen is linked to the iPad with Google Jamboard running and could be shared on Blackboard Collaborate. Here I offer my first tip:
Explore tools available to design workflows for content delivery and decide on one that works well
Days before the problems class, you wonder whether you have done enough preparation. Have you read through and completed the problem sheet; ready to answer those burning questions from the students you will be demonstrating for? It is important you…
Figure 2: Snippet of type-written worked solutions for the Atmospheric Physics MSc module.
Have your worked solutions to refer to during class
A good way to ensure you are able to resolve queries about problem sheet questions is to have a version of your own working. This could be as simple as some written out points, or in my case, fully type-written solutions, just so I have details of each step on hand. In some of my fully worked solutions, I added comments for steps where I found the learning curve was quite steep and annotated places where students may run into potential problems.
Students seem to take interest in these worked solutions, but here I must recommend…
Do not send out or show your entire worked solutions
It is arguable whether worked solutions will help students who have attempted all problems seriously, but the bigger issue lies in those who have not even given the problems a try. As a demonstrator, I often explain the importance of struggling through the multiple steps needed to solve and understand a physics problem. My worked solutions usually present what I consider to be the quick and more refined way to the numerical solution, but usually are not the most intuitive route. On that note, how then are you supposed to help someone stuck on a problem?
It may be tempting to show snippets of your solutions to help someone stuck on a certain part of a problem. Unfortunately, I found this did not work very well. Students can end up disregarding their own attempt and copy down what they regard as the “model answer”. (A cheeky student would have taken multiple screenshots while I scrolled through my worked solutions on the shared screen…) What I found worked better in breakout groups was for the student(s) to explain how they got stuck.
For example, I once had a few students ask me how they should work out the boiling temperature from saturated vapour pressure using Tetens’ formula. However, my worked solutions solved this directly using the Clausius-Clapeyron equation. Instead of showing them my answer, I arrived at the point where they got stuck (red in Figure 3), essentially putting myself in their shoes. From that point, I was able to give small hints in the correct direction. Using their method, we worked together towards a solution for the problem (black in Figure 3). Here is another tip:
Work through the problem from your students’ perspective
Figure 3: Google Jamboard slide showing how Tetens’ formula is rearranged. Red shows where some students got up to in the question, whereas black is further working to reach a solution.
This again illustrates the point on there being no “model answer”. As in many scientific fields, there exist multiple path functions that get you from a problem to a plausible solution, and the preference for such a path is unique to us all.
There will always be a group of diligent students who gave the problem sheet a serious attempt prior to the class. You will find they only take less than 30 minutes to check their understanding and numerical solutions with you, and they might do their own thing afterwards. This is the perfect opportunity to…
Present bonus material to stretch students further
Some ideas include asking for a physical interpretation from their mathematical result, or looking for other (potentially more efficient) methods of deriving their result. For example, I asked students to deduce a cycle describing the Stirling engine on a TS diagram, instead of the pV diagram they had already drawn out as asked by the problem sheet.
Figure 4: A spreadsheet showing the content coverageof each past exam question
I also have a table of past exam questions, with traffic light colours indicating which parts of the syllabus they cover. If a student would like to familiarise themselves with the exam style, I could recommend one or two questions using this spreadsheet.
On the other hand, there may be the occasional group who have no idea where equation (9.11) on page 168 of the notes came from, or a student who would like the extra-reassurance of more mathematical help on a certain problem. As a final tip, I try to cater to these extra requests by…
Staying a little longer to answer a final few questions
The best demonstrators are approachable, and go the extra mile to cater to the needs of the whole range of students they teach, with an understanding of their perspectives. After all, being a demonstrator is not only about students’ learning from teaching, but also your learning by teaching!
I would welcome your ideas about demonstrating as a PhD. Feel free to contact me at email@example.com if you would like to discuss!
As per annual tradition in the Meteorology Department, PhD students have chosen a distinguished scientist to visit the department for one week. Previous years’ visitors include Prof. Tapio Schneider (Caltech), Prof. Olivia Romppainmen-Martius (University of Bern), and Prof. Cecilia Bitz (University of Washington). This year’s winning vote was New York University’s Prof. Laure Zanna, who will be visiting the department virtually1 between 2 – 6th November.
Laure is an oceanographer and climate scientist whose career so far has spanned three continents, won her an American Meteorological Society (AMS) Early Careers’ award for “exceptionally creative” science this year, and netted her 600 citations in the last two years. Her research interests encompass ocean turbulence, climate dynamics, predictability, machine learning and more. Some of the many topics of her published papers include the uncertainty in projections of ocean heat uptake; ocean turbulence parametrisations; predictions of seasonal to decadal sea surface temperatures in the Atlantic using simple statistical models and machine learning to inform prediction of extreme events. Besides being an exceptional scientist, speaker and educator, Laure is a down-to-Earth and friendly person, described by the Climate Scientists podcast’s Dan Jones as ‘a really great person who helps to tie the whole community together’.
As someone who had received their PhD only just over a decade ago, we thought Laure would be the perfect candidate to inspire us and our science through sharing some of her academic experiences with us. Before her visit next week, Laure kindly answered some interview-style questions for this week’s Social Metwork blog post.
Q: What inspired you to research oceanography and climate in the first place?
A: I always enjoyed math and physics. The possibility of using these disciplines to study scientific problems that I could “see” was very appealing.
Q: Why were you drawn to machine learning?
A: The power of machine learning (ML) to advance fields such as natural processing language or computer science is indisputable. I was excited by the premise of ML for climate science. In particular, can ML help deepen our understanding of certain aspects of the climate systems (e.g. interactions between scales or interactions between the ocean and atmosphere)? Can ML improve the representation of small-scale processes in climate models? ML, by itself, is not enough but combined with our physical understanding of the climate system could push the field forward.
Q: Can you give us an idea of what’s the most exciting research you are working on right now?
A: This is impossible. I work on 2 main areas of research right now: understanding and parameterizing ocean mesoscale eddies and understanding the role of the oceans in climate. I am passionate and excited about both topics. Hopefully, you will hear about both of them during the week.
Q: When did you realise/decide you were going to remain in academia?
A: I decided that I wanted to try and stay in academia in the last year of my PhD. I was lucky enough to be able to.
Q: What is your favourite part of your job?
A: Working with my group! The students and postdocs in the group have different expertise but all are passionate about their research. They make the work and the research more fun, more challenging, and more inspiring.
We are honoured to have our invitation accepted by Laure and are eagerly anticipating answers to more of these kind of questions throughout next week’s conversations. Laure will be presenting a seminar titled, “Machine learning for physics-discovery and climate modelling” during the Monday Departmental Seminar series, as well as another seminar in the Climate and Ocean Dynamics research group, titled “Understanding past and future ocean warming”. She will also give a career-focused session at PhD group and, of course, engage with both the PhD students and staff on an individual basis during one-to-one meetings. We are grateful and delighted to be able to welcome Laure to the Meteorology department despite the various difficulties the year 2020 has posed on everyone, so come along to next week’s events!
Have you been thinking ‘I’ll never be good enough for a PhD’? Or perhaps you’ve been set on the idea of joining those who push the bounds of knowledge for quite some time, but are feeling daunted by the process? Well, keep reading.
I started university with the hopes of stretching myself academically and gaining an undergraduate degree. As the degree progressed, I found myself increasingly improving in my marks and abilities. I enjoyed the coursework – researching a topic and the sense of discovery brought about by it. I became deeply interested in climate change and the impact humans have on the environment and was able to begin my dissertation research a year early because I was so motivated within my subject.
In my final year of undergraduate studies, much of my time was pre-occupied with my role as Student President. Attending social events, board meetings, and lots of other things that didn’t involve a darkened room and a pile of books. I was very much a student who turned up, put the effort in, and then spent the rest of my time as I wished.
Giving a speech at the Global Youth Strike for Climate, Inverness, as Student President. Extracurricular activities are a worthwhile addition to your application and were considered a lot during the interview!
I began to look for opportunities for research degrees online, as well as asking almost anyone and everyone I knew academically if they had any ideas. Nothing came to fruition. That was until I received a Twitter notification from my lecturer drawing my attention to what looked to be an ideal PhD studentship. The snag? Applications were due to close within 3 hours of me checking the notification.
By the time I had read the project particulars, accessed the cited literature and paced around my living room more than a few times, I had around 2 hours to submit an application. Due to my prior unsuccessful searches, I hadn’t previously submitted a PhD application and so had nothing to refer to – but proceed I did.
Thankfully, the application was relatively straightforward. Standard job application information, details of the grades I had achieved and was predicted to achieve, and two academic references (for me, my personal academic tutor and climate change lecturer). What took time (I would advise anyone considering an application to prepare these earlier than I did!) was the statement of research interest and academic CV. My university careers service had excellent advice and resources to assist in that regard.
Within minutes of the deadline, my application was in. I had almost forgotten about it by the time a week-or-so later I received an e-mail inviting me to Reading for an interview day. Shocked and excited were the emotions – little old me from the Highlands of Scotland, who hadn’t yet finished his undergraduate degree, was somehow being invited to one of the best Meteorology departments in the world to interview for a PhD studentship.
No time to spare, my travel to and from Reading was booked. For the next couple of weeks, all I now had to worry about was how to do a PhD interview – though as will become clear, I need not have worried. I sought the advice of academic friends and colleagues (a calming influence for sure) and countless websites and forums (generally a source of unnecessary worry).
Given the level of conflicting advice on PhD interviews, on arrival at Reading I wasn’t sure what to expect. At the front door I was provided with all the information that I needed for the day. I then made my way to a room with all the other candidates for a welcome talk and the opportunity to learn more about other projects on offer over lunch.
The interview itself was very relaxed. No ‘stock’ PhD interview questions here – it was very much an opportunity to discuss my previous work and abilities, and how that might fit with the project. Importantly, it was an opportunity to meet my potential supervisors and ‘interview’ them too. If you’re going to spend 3-4 years working together, the connection needs to work well both ways. So, whilst the 30-minute interview slot seemed daunting on paper, the time flew by and it was soon time to leave.
Fast forward a week or so and I was very surprised to receive an e-mail offering me the studentship that I had applied for: Developing an urban canopy model for improved weather forecasts in cities. And the rest, as they say, is history.
At my desk in the Department of Meteorology, University of Reading.
I hope that this blog post has helped you to feel less daunted to begin your PhD journey. Please feel free to get in touch with me by e-mail if you would like to chat further about beginning a PhD, or indeed to let me know how your own interview goes. Good luck!
As PhD students, working from home is an option for many of us on a “normal” day – as indeed is increasingly the case with jobs which primarily need just an Internet connection. But, thanks to COVID-19, working from home (WFH) is our new collective reality. So how can we make this work well, when for many, our offices may only now be a few steps away from our beds? We asked around for advice on this matter from current PhD students.
Remember to take a break every half an hour or so. Go away from the desk!
It can be easy to forget to take a break when you’re “at home”, even if you’re also “at work”, and especially when you’re likely closer to the kettle/food/toilet than you would be otherwise. Get up, move around!
Stick to a regular schedule: when you wake up, go to sleep, work, relax, etc.
This is great advice for doing a PhD in general, but even more pertinent now that our routines have been turned upside down.
Pretend that you “go to and from work”, i.e take a morning and afternoon walk/cycle to mark the start and end of your work day.
A commute can be a great time to wake up in the morning and wind down in the evening. Get creative with what you can (safely, and in accordance with government guidance) do to replace your commute during this time.
Pretend that you go to work by dressing accordingly, it makes the brain active and makes you stronger against the ‘do something else’ or ‘ relax’ mode activated by the comfy at home clothes.
It’s tempting to work wearing pyjamas, but will this help your productivity and mindset? Getting dressed for work can also help to maintain your work-life balance.
Look after your posture. If possible, sit at a desk with a screen at the right height.
Try to follow standard health and safety advice when it comes to working long hours at a desk. If possible, invest time and money in making your home working environment a comfortable and non-straining place to be.
If you can at all help it, don’t work in the room where you sleep. It can cause difficulties sleeping.
This also helps add some breaks and changes in your day, which can help to maintain focus and motivation.
Enjoy the benefits of working from home: take a break to actually cook lunch, get things done around the house. Let yourself appreciate the things that are handy about it as well as the negatives.
Being able to get away from your work and do something like ironing, cooking, baking or cleaning might actually help your productivity and concentration by providing a better break than you might otherwise get in an office. Embrace it!
Schedule social e-contact. Don’t let yourself go more than a day without at least hearing someone’s voice on the phone. Use the opportunity to reconnect with old friends.
In Reading, we’re making extensive use of Microsoft Teams to remain in contact with each other and try to mimic our vibrant work atmosphere.
Do (as long as it’s safe to do so) go for walks, head outside, make sure you do some exercise twice a week.
Luckily, we’ve got some very nice weather this week in most of the UK. But do please adhere to social distancing guidelines when you do go outside.
It can be easy for the lines between work and life outside of work to be blurred during a PhD at the best of times, and WFH can make this more problematic. Set your hours, and stick to it.
If you work 8-4, work 8-4! At 4pm, switch your computer off and do something different. Without an evening commute, it can be trickier to bring an end to your working day, but this is probably one of the most important things to maintain.
Most operating systems, including Windows 10, support multiple virtual desktops. Try using one of those for your virtual “work” PC, and another as your virtual “home” PC. Then you can keep the two segregated.
At the end of the day you can switch to your “home” desktop, and then return to “work” the following day.
The new academic year is now underway, and a new bunch of eager first year PhD students are dipping their toes into a three-to-four year journey to their doctorate. So, we’ve collated some advice from the more experienced among us! The idea behind the following tidbits of advice is that they are things we would tell our younger selves if we could go back to day 1…
“Make sure you and
your supervisor set out expectations and at least a vague timeline at the
start, that way you will know you’re on track.”
“Write code as if
you’re giving it to someone else – one day you might have to.”
Even if you don’t give your code to another use, in a year’s time you’ll have forgotten what it does! Related to this, it’s useful to keep good “readme” documents to note where all your code is, how to run things, etcetera. Also, if you think you’re going to present a plot at some point – in a talk, paper, or even your thesis, make a final version at the time (using appropriately accessible colour maps and big enough labels), plus note down where you’ve stored the code you used to make it.
“Learn and use
git/github (or at least get familiar with the 3 basic commands of: git add,
commit, push) ASAP! This means that if you take a wrong turn in your code (you
will), you can painlessly ‘revert’ to a stage before you made a mess.”
“Read papers with your literature review in mind. If you can’t see where the paper will fit in your literature review, either reconsider your literature review… or find a more relevant paper.”
“Write down everything you learn, or facts you are told – you never know when you’ll need a piece of information again.”
But also be
prepared to have not really followed any of this advice properly until you
regurgitate it to new students in your fourth year and wonder why you haven’t
been doing any of it up until now.
“Try to keep up a good routine – it’s much easier to get out of bed when you’re having a slow work week if that’s what your body is used to.”
“You’ll be amazed at
how much you’ll learn and master without even realising.”
“Don’t compare yourself to others.”
Every PhD project is unique, as is every student. During a PhD, you’re looking into the unknown. Maybe you’ll get lucky (with some hard work) and have some really interesting results, or it might be a bit of a battle. Some projects are more suited to regular publications, others less so – this doesn’t necessarily reflect your individual abilities. In addition, everyone has different background knowledge and motivation for doing a PhD.
“Not every day has to
be maximum productivity, that’s okay!”
“Some days are great, others are rubbish. Like
“Make friends with other PhD students. It’s nice to have someone who might make you cake when you feel sad, or happy.”
This is so true. A PhD is quite a unique experience and lots of people don’t really get it, thinking it’s just like another undergrad. Sometimes it’s really useful to have someone who understands the stress of some code just not working, or the dread of a blank page where your monitoring committee report should be. It’s also helpful to get to know people in the years above, or even post-docs, since they’ve probably already gone through what you’re experiencing.
“Make friends and join clubs and societies with people that aren’t doing PhDs.”
Sometimes it’s important to get out of the PhD “bubble” and put things in perspective. Keeping in touch with friends that have “real” jobs (for want of a better word) can be a nice reminder of some of the benefits of PhD life – such as flexible hours (you don’t have to be in before 9 every day) or not having to wear formal business attire.
“Try to keep your weekends free – it’s great for your sanity!”
“Take holiday! You are
“Don’t feel guilty for not cheering up when people tell you everything’s okay. It almost invariably is, but sometimes it all gets a bit much and you’ll feel bad for a while, that’s totally normal!”
Yes, it’s totally okay to have a couple of bad days. Remember, this can often be true of people with ‘real’ jobs, it isn’t just unique to the PhD experience! However, if you’re feeling bad for a long period of time, it’s important to acknowledge that this isn’t okay and you don’t have to feel like that. It might be helpful to let your supervisor know that you’re having a bit of a hard time, for whatever reason, and work might be slow for a while. There are also lots of support systems available. For students at Reading, you can find out more about the Counselling and Wellbeing Service here (http://www.reading.ac.uk/cou/counselling-services-landing.aspx). A PhD is hard work, but it should be a fundamentally enjoyable experience!
“No poking your supervisor with a stick. They don’t appreciate it.”
(…no, we don’t get it either)
Co-written by Simon Lee and Sally Woodhouse, with anonymous pieces of advice collected from various PhD students in the Department of Meteorology.
With thanks to all my helpers who enabled the week to go smoothly! Adam Bateson, Sally Woodhouse, Kaja Milczewska and Agnieszka Walenkiewicz
Each year PhD students in the Department of Meteorology invite a distinguished scientist to spend a week with us.This year we invited Prof. Cecilia Bitz, who visited between the 28th-31st May. Cecilia is based at the University of Washington, Seattle.
Cecilia’s research interests are the role of sea ice in the climate system, and high latitude climate and climate change. She has also done a lot of work on the predictability of Arctic sea ice, and is involved in the Sea Ice Prediction Network.
The week began with a welcome reception in the coffee area, introducing Cecilia to the department, followed by a seminar by Cecilia on ‘Polar Regions as Sentinels of Different Climate Change’. The seminar predominantly focused on Antarctic sea ice, and the possible reasons why Antarctic sea ice behaviour is so different to the Arctic. Whilst Arctic sea ice has steadily declined we have seen Antarctic sea ice expansion over the past four decades, with extreme Antarctic sea ice extent lows since 2016.
Throughout the week Cecilia visited a number of the research groups, including Mesoscale, HHH (dynamics) and Cryosphere, where PhD students from each group presented to her, giving a taste of the range of PhD research within our department.
Cecilia gave a second seminar later in the week in the Climate and Ocean Dynamics (COD) group meeting, this time focusing on the other pole, ‘Arctic Amplification: Local Versus Remote Causes and Consequences’. Cecilia discussed her work quantifying the role of feedbacks in Arctic Amplification, how they compare with meridional heat transports, and what influence Arctic warming has on the rest of the globe.
On Wednesday afternoon the normal PhD group slot consisted of a career discussion, with Cecilia. Cecilia shared some of her career highlights with us, including extra opportunities she has taken such as doing some fieldwork in Antarctica and working for the charity, Polar Bears International, her insights and advice from her own experiences, as well as about post-doctoral opportunities in the US. A few of my personal take-aways from this session were to try give yourself space to learn one new thing at a time in your career (e.g. teaching, writing proposals, supervising etc). Try to work on a range of problems, and keep your outlook broad and open to new ideas and approaches. Take opportunities when they appear, such as fieldwork or short projects/collaborations.
A small group of PhDs also met with her on the Friday to have an informal discussion about climate policy. In particular about her experiences speaking to the US senate, being a part of the IPCC reports and about the role of scientists in speaking about climate change, and whether we have a responsibility to do so.
Thursday evening the PhDs took Cecilia to Zero Degrees (a very apt choice for a polar researcher!), and enjoyed a lovely evening chatting over pizza and beer.
The week ended with a farewell coffee morning on Friday, where we gave Cecilia some gifts to thank her for giving us her time this week including some tea, chocolates, a climate stripes mug and a framed picture of us…
All the PhDs had a great week. We hope Cecilia enjoyed her visit as much as we did!
Having been a PhD student for a little over 3 months I am perhaps ill-qualified to write such a ‘PhD tips’ type of blog post, but write one I appear to be doing! It’s probably actually more accurately titled ‘study tips in general but ones which are highly relevant to science PhDs.’
The following are just my tips on what have helped me over the course of my studies and may be obvious or not suitable for others, but I write them on the off-chance that something here is useful to someone out there. No doubt I will have many more such strategies by the end of my time here in Reading!
Papers and articles As a science student you may have encountered these from time to time. The better ones are clearly written and succinct, the worse ones are verbose and obscurantist. If you’re not the quickest reader in the world, getting through papers can end up consuming a great deal of your time.
I’m going to advocate speed reading in a bit but when you start learning speed reading, one of the things they ask you to think about first is “Do I really need to read this?”. If the answer is yes, then the next question is “Do I really need to read all of it?”. Perhaps you only need to glance at just the abstract, figures and conclusion? After all, time spent reading this is time not spent doing something else, something more profitable perhaps, so do check that it really is worth your time before diving in.
So once I’ve ascertained that the article is indeed worth my time, I sit down with a pencil (or the equivalent for a PDF) and read through the sections I’ve decided on. Anything that makes my neurons spike (“oh that’s interesting….”), I underline or highlight. Any thoughts or questions that occur to me, I write in the margin. If I feel the need to criticise the paper for being insufficiently clear then I write down these remarks, too.
Once I get to the end, I put the article away out of sight and sit down with a blank piece of paper (or on a computer) and try and write something very informally about what I’ve just read. Quite often my mind will go helpfully blank at this point, so I try and finish the following sentence: “The biggest thing (if anything) I learned from this article was….”. Completing this one sentence then tends to lead to other stuff tumbling out and in no particular order I jot these all down. Only once the majority of it is down on paper do I take a peek at the annotated piece to see what I missed (For heaven’s sake avoid painting the article yellow with a highlighter!)
This personal blurb that you have produced is then a good way to quickly remind yourself of the contents of that article in the future without having to reread it from scratch. This post-reading exercise need not take more than 15 minutes but if you’re worried about spending this extra time, don’t be. You’ll save yourself a heap of time in future by not having to reread the damn thing.
Random piece of advice – if you are unaware of the Encyclopedia of Atmospheric Sciences, then check it out. Whatever your PhD topic I guarantee there’ll be 10 or so shortish entries which are all highly relevant to your particular PhD topic and consequently worth knowing about!
Speed reading Really still on the previous paragraph but as is often the way, between the valuable articles that you really should be reading and the stuff for which life’s really too short there’s a grey area. For such grey areas I am an advocate of speed reading. For any electronic texts check out this free website:
The pace the words flash up doesn’t have to be particularly fast (I suggest trying 300 wpm to start with) but the golden rule is to never press pause once you’ve started. No going back to read stuff you’ve missed (well not until you’ve reached the end first at least!). This method of reading is especially useful for any articles that feel like quagmires into which you are slowly drowning. Paradoxically reading faster in such instances often increases one’s comprehension.
A good way to develop the skill of speed reading is to start on articles you see posted on social media, articles that you are not too fussed about getting every single detail. Just let it wash over you!
Talks and lectures I have found it useful to make audio recordings of these. I don’t usually tend to listen back, but if there is something that was particularly interesting or dense that might be worth revisiting then it can be very worthwhile. I make a note of the time this something was said at the time it was said and can thus track it down in the recording fairly painlessly afterwards.
One tip about note taking that has stayed with me since I first heard it several years back was the following: after writing down the title, only make notes on what is surprising or interesting to you, just that! This may result in many lines of notes or no lines at all, but whatever you do, don’t just make notes of everything that was said. This advice has been very useful for me.
Organising Ask me in person if you would like to know my thoughts on this.
Programming to help physical intuition. This is probably more relevant to students like me who didn’t come from a maths or physics undergrad and consequently aren’t quite as fluent in the old maths….or perhaps undergrads for that matter… ….but in my undergrad (environmental science) I spent quite a lot of the time spent studying maths (and to a lesser extent) physics involved memorising complicated procedures. The best example of this was a lecture on Fourier Series where the professor took the whole hour to work through the process of getting from an input (x^2) to the output (first n terms of the Fourier series). Because it took so much space/effort for me to remember this lengthy process, it ended up crowding out the arguably more important conceptual stuff, such as what a Fourier series actually does and why it is it so useful. When all is said and done and the final exam is handed in, these concepts are what should (ideally) stick with you even if the details of how, don’t. So here’s where I think programming can come in. Firstly, there’s nothing like coding up some process to check whether you understand the nuts and bolts of it, but more importantly once it has been coded up properly you can then play about with the inputs to see how these affect the graphed outputs. Being able to ‘play’ about like this gives you a more intuitive feel for the model/process that wouldn’t be possible if you had to manually redo the laborious calculations each time you wanted to change the input parameters. 3 examples of where I have done this myself are the following: 1. Getting my head around the thermal inertia of the oceans by varying the depth of the surface and deep layer of the ocean in a simple model. 2. Playing around graphically with dispersion. 3. Convincing myself that it really is true that in the middle of the Northern Hemisphere summer the north pole receives more energy per day than the equator.
And you? So do you have any hard won study/research tips? If so do email me as I would be interested in hearing about them! Which study hack do you think I (or others) are most lacking?
Now that Christmas is just around the corner, and us first year students have settled in to the swing of things, I thought it would be nice to write a short piece on what it’s like starting a PhD.
Reflecting on my experience so far, my first thought was to remark at how I’d only been a PhD student for a little over two months. Frankly, I don’t think I’ve ever learnt so much in quite so short a time before. This was an encouraging thought, because often in the moment progress can seem very slow indeed. However, when you put it all together and zoom out a little, you realise how far you’ve come. If you feel like you’ve wasted a day lost in code, or just generally lost, it’s never wasted; it’s your PhD and is a constant learning process. I’ve found it really rewarding sometimes to simply explore what I find interesting, or practice different ways of making figures. I would recommend that if something, anything, sparks your interest, investigate it, and read up about it. If it comes to nothing, or if you’re not ready to write a journal paper at the end of the day, well, the skills and familiarities you picked up may eventually go towards doing so! Don’t be afraid of not doing the perfect job first time or making a mistake.
The day-to-day life of doing a PhD is also very dynamic. Perhaps I thought I would only spend long hours sitting at my desk in front of a monitor, but every day is different. Research groups, seminars, and social activities really add variety and inspiration to each and every day, even if it means pulling myself away from my data for a small amount of time! I’d recommend to any first year to get involved as much as possible. It is the best way to get to know people in your department and beyond and how they do science! I will admit it took me a little while at first to treat all these different aspects of a typical day as “PhD work”, but that’s the right mindset to be in.
Finally, getting to know other PhD students and researchers has been one of the best and most eye-opening elements so far. In fact, I would say it is almost a crucial part of the process. I am always grateful to those higher up the academic chain, who, despite being busy, are continually happy to offer advice from the small to the big things. At first, I felt like I would be some sort of annoyance asking other people for help on something, or that I should be able to work it out for myself. However, one comes to realise everyone is delighted to help and share their expertise with you. That’s what being a scientist is about, right? As Google Scholar reminds us on each visit: “Stand on the shoulders of giants.” Those older PhDs may scoff at being referred to as giants, but to someone starting a PhD, daunted at how far there is to go and how much there is to do, well… they’ve done a large part of it!
It’s not possible to always see the positives all the time, especially with research. However, one thing is for sure: you not only grow huge amounts as a scientist, but generally as a person, and I think if you keep that in mind, it all makes that little bit more sense.
Have you ever run into a memory error or thought your function is taking too long to run? Here are a few tips on how to tackle these issues.
In meteorology we often have to analyse large datasets, which can be time consuming and/or lead to memory errors. While the netCDF4, numpy and pandas packages in Python provide great tools for our data analysis, there are other packages we can use, that parallelize our code: joblib, xarray and dask (view links for documentation and references for further reading). This means that the input data is split between the different cores of the computer and our analysis of different bits of data runs in parallel, rather than one after the other, speeding up the process. At the end the data is collected and returned to us in the same form as before, but now it was done faster. One of the basic ideas behind the parallelization is the ‘divide and conquer’ algorithm [Fig. 1] (see, e.g., Cormen et al. 2009, or Wikipedia for brief introduction), which finds the best possible (fastest) route for calculating the data and return it.
The simplest module we can use is joblib. This module can be easily implemented for for-loops (see an example here): e.g. the operation that needs to be executed 1000 times, can be split between 40 cores that your computer has, making the calculation that much faster. Note that often Python modules include optimized routines, and we can avoid for-loops entirely, which is usually a faster option.
The xarray module provides tools for opening and saving multiple netCDF-type (though not limited to this) datasets, which can then be analysed either as numpy arrays or dask arrays. If we choose to use the dask arrays (also available via dask module), any command we use on the array will be calculated in parallel automatically via a type of ‘divide and conquer’ algorithm. Note that this on its own does not help us avoid a memory error as the data eventually has to be loaded in the memory (potentially using a for-loop on these xarray/dask arrays can speed-up the calculation). There are often also options to run your data on high-memory nodes, and the larger the dataset the more time we save through parallelization.
In the end it really depends on how much time you are willing to spend on learning about these arrays and whether it is worth the extra effort – I had to use them as they resolved my memory issues and sped up the code. It is certainly worth keeping this option in mind!
If the following paths are not ‘unset’ then you need to unset them (check this with command: conda info -a):
unset PYTHONPATH PYTHONHOME LD_LIBRARY_PATH
In python you can then simply import xarray, numpy or dask modules:
import xarray as xr; import dask.array as da; import numpy as np; from joblib import Parallel, delayed; # etc.
Now you can easily import datasets [e.g.: dataset = xr.open_dataset() from one file or dataset = xr.open_mfdataset() from multiple files; similarly dataset.to_netcdf() to save to one netcdf file or xr.save_mfdataset() to save to multiple netcdf files] and manipulate them using dask and xarray modules – documentation for these can be found in the links above and references below.
Once you open a dataset, you can access data either by loading it into memory (xarray data array: dataset.varname.values) and further analyzing it as before using numpy package (which will not run in parallel); or you can access data through the dask array (xarray dask array: dataset.varname.data), which will not load the data in the memory (it will create the best possible path to executing the operation) until you wish to save the data to a file or plot it. The latter can be analysed in a similar way as the well-known numpy arrays, but instead using the dask module [e.g. numpy.mean (array,axis=0) in dask becomes dask.array.mean (dask_array,axis=0)]. Many functions exist in xarray module as well, meaning you can run them on the dataset itself rather than the array [e.g. dataset.mean(dim=’time’) is equivalent to the mean in dask or numpy].
Caution: If you try to do too many operations on the array the ‘divide and conquer’ algorithm will become so complex that the programme will not be able to manage it. Therefore, it is best to calculate everything step-by-step, by using dask_array.compute() or dask_array.persist(). Another issue I find with these new array-modulesis that they are slow when it comes to saving the data on disk (i.e. not any faster than other modules).
I would like to thank Shannon Mason and Peter Gabrovšek for their helpful advice and suggestions.
Cormen, T.H., C.E. Leiserson, R.L. Rivest, C. Stein, 2009: An introduction to algorithms. MIT press, third edition, 1312 pp.