advice

Starting Your PhD Journey: Tips for Success

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So, you’ve officially embarked on the exciting journey that is a PhD—congrats! You’ve reached a major milestone, and whether you’re feeling excited, overwhelmed, or a mix of both, just know you’ve signed up for an adventure like no other. A PhD is an incredible opportunity to dive headfirst into a subject you’re passionate about, build a toolkit of valuable skills, and—who knows?—maybe even make history in your field.

But let’s be real: it’s not all rainbows and groundbreaking discoveries. The PhD life can be challenging, sometimes feeling like a marathon through an obstacle course. You’ll have moments that test your patience, confidence, and sometimes, your sanity. That’s why here at Social Metwork, we’ve gathered some golden advice from seasoned PhD students to help you navigate these waters. Our goal? To make this transition into PhD life a little smoother, maybe even a little fun.

We’ll break these tips down into three areas: navigating day-to-day life as a PhD student, getting organized like a pro, and growing into the great scholar you’re destined to be. Ready? Let’s dive in!

1. Navigating Day-to-day Life as a PhD Student

Work-life balance

The first year of your PhD can feel overwhelming as you try to juggle research, coursework, and life. One key piece of advice? Don’t overwork yourself. As Laura Risley puts it, “Sometimes if you’re struggling with work, an afternoon off is more useful than staying up late and not taking a break.” It’s easy to get absorbed in your work, but stepping away to recharge can actually help you return with fresh perspectives.

Getting involved in activities outside your PhD is another great way to maintain balance (L. Risley, 2024). Whether it’s exploring more of Reading, participating in a hobby, or just getting outside for some fresh air, your brain will thank you for the break. Remember, “Your PhD is important, but so is your health,” so make sure to take care of yourself and make time for things that bring you joy: exercise, good food, and sleep!

Lastly, don’t underestimate the power of routine. Building a consistent schedule can help bring some stability to PhD life. Most importantly, be kind to yourself. The weight of expectations can be heavy so give yourself permission to not have it all figured out yet. You won’t understand everything right away, and that’s completely normal!

Socialising and Building a Support System

Your cohort is your lifeline. The people you start with are going through the same experiences, and they will be your greatest support system. Whether you’re attending department events, organizing a BBQ, or just grabbing a coffee, socializing with your peers is a great way to get through everything. At the end of the day, we are all in this together! As Rhiannon Biddiscombe wisely says, “Go for coffee with people, go to Sappo, enjoy the pub crawls, waste a night out at PT, take part in the panto, spend time in the department in-person” — so make sure you get involved!

If what you want is to meet new people, you could even help organise social events, like research groups or casual hangouts – feeling connected within your department can make all the difference when you’re having a tough week. And hey, if you’re looking for a fun group activity, “Market House in town has darts boards, ping pong tables, and shuffleboard (you slide little discs to the end of the board, it’s good fun!)”.  

2. Getting Organised Like a Pro

Writing and Coding

Staying organised is critical for both your mental health and your research. Adam Gainford recommends you start by setting up a reference manager early on—trust us, you’ll thank yourself later. And if your research involves coding, learn version control tools like GitHub to keep your projects neat and manageable. As a fellow PhD student says “Keeping organised will help keep your future self sane (and it’s a good skill that will help you with employability and future group projects)”.

A golden rule for writing: write as you go. Don’t wait until the last minute to start putting your thoughts on paper. Whether it’s jotting down a few ideas, outlining a chapter, or even starting a draft, regular writing will save you from stress later on. Remember what Laura always says, “It’s never too early to start writing.”

Time Management

Managing your time as a PhD student is a balancing act. Plans will shift, deadlines will change, and real life will get in the way—it’s all part of the process. Instead of stressing over every slipped deadline, try to “go with the flow”. Your real deadlines are far down the road, and as long as you’re progressing steadily, you’re doing fine.

Being organised also doesn’t have to be complicated. Some find it helpful to create daily, weekly, or even monthly plans. Rhiannon recommends keeping a calendar is a great way to track meetings, seminars, and research group sessions – I myself could not agree more and find time-blocking is a great way to make sure everything gets done. Regarding your inbox, make sure you “stay on top of your emails but don’t look at them constantly. Set aside a few minutes a day to look at emails and sort them into folders, but don’t let them interrupt your work too much!”. Most importantly though, don’t forget to schedule breaks—even just five minutes of stepping away can help you reset (and of course, make sure you have some valuable holiday time off!).

3. Growing into the Scholar You’re Meant to Be

Asking for Help

This journey isn’t something you’re expected to do alone. Don’t be afraid to reach out for help from your friends, supervisors, or other PhD students. Asking questions is a sign of strength, not weakness. What’s great is that everyone has different backgrounds, and more often than not, someone will be able to help you navigate whatever you’re facing (trust me, as a geography graduate my office mates saved my life with atmospheric physics!). Whether you’re stuck on a tricky equation or need clarification on a concept, ask ask ask! 

“You’ve got a whole year to milk the ‘I’m a first year’ excuse, but in all seriousness, its never too late to ask when you’re unsure!” – a fellow PhD student.

Navigating Supervisor Meetings

Your supervisors are there to guide you, but communication is key. Be honest with them, especially when you’re struggling or need more support. If something doesn’t make sense, speak up—don’t nod along and hope for the best, “they should always have your back” (it will also be very embarrassing if you go along with it and are caught out with questions…). 

Also, “If you know some things you want to get out of your PhD, communicate that with your supervisors”. Open communication will help you build a stronger working relationship and ensure you get what you need from the process.

Dealing with Imposter Syndrome

Imposter syndrome can hit hard during a PhD, especially when you’re surrounded by brilliant people doing impressive work. But here’s the thing: don’t compare yourself to others. Everyone’s PhD is different—some projects lend themselves to quick results, while others take longer. Just because someone publishes early doesn’t mean your research is less valuable or that you’re behind – we are all on our own journeys. 

And remember, no one expects you to know everything right away. “There might be a pressure, knowing that you’ve been ‘handpicked’ for a project, that you should know things already; be able to learn things more quickly than you’re managing; be able to immediately understand what your supervisor is talking about when they bring up XYZ concept that they’ve been working on for 20+ years. In reality, no reasonable person expects you to know everything or even much at all yet. You were hand-picked for the project because of your potential to eventually become an independent researcher in your field – A PhD is simply training you for that, so you need to finish the PhD to finish that training.”

If you’d struggling with imposter syndrome, or want to learn about ways to deal with it, I highly recommend attending the imposter syndrome RRDP. 

A Few Final Words of Wisdom

The PhD rollercoaster is full of ups and downs, but remember, you’re doing fine. “If you’re supervisors are happy, then don’t worry! Everything works out in the end, even when it seems to not be working for a while! “– Laura Risley

It’s also super important to enjoy the process. You’ve chosen a topic you’re passionate about, and this is a rare opportunity to fully immerse yourself in it. Take advantage of that! Don’t shy away from opportunities to share your work. Whether it’s giving a talk, presenting a poster (or writing for the Social Metwork blog!!), practice makes perfect when it comes to communicating your research.

Embarking on a PhD is no small feat, but hopefully with these tips, you’ll have the tools to manage the challenges and enjoy the ride. And if all else fails, remember the most important advice of all: “Vote in the Big Biscuit Bracket—it’s the most important part of being a PhD student!”. 

From the department’s PhDs students to you! 

Written by Juan Garcia Valencia 

Faster analysis of large datasets in Python

Lina Boljka1 Comment

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.

divide_and_conquer
Figure 1: A simple example of the ‘divide and conquer’ algorithm for sorting a list of numbers. First the list is split into simpler subproblems, that are then solved (sorted) and merged to a final sorted array. Source. 

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!

Getting started with xarray/dask

In the terminal window:

  • Use a system with conda installed (e.g. anaconda)
  • To start a bash shell type: bash
  • Create a new python environment (e.g. ‘my_env’) locally, so you can install custom packages. Give it a list of packages:
    • conda create -n my_env xarray
  • Then activate the new python environment (Make sure that you are in ‘my_env’ when using xarray):
    • source activate my_env
  • If you need to install any other packages that you need, you can add them later (via conda install), or you could list them with xarray when you create the environment:
    • conda install scipy pandas numpy dask matplotlib joblib #etc.
  • 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.

References

Cormen, T.H., C.E. Leiserson, R.L. Rivest, C. Stein, 2009: An introduction to algorithms. MIT press, third edition, 1312 pp.

Dask Development Team, 2016: Dask: Library for dynamic task scheduling. URL: http://dask.pydata.org

Hoyer, S. & Hamman, J., 2017. xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5, p.10. DOI: http://doi.org/10.5334/jors.148

Hoyer, S., C. Fitzgerald, J. Hamman and others, 2016: xarray: v0.8.0. DOI: http://dx.doi.org/10.5281/zenodo.59499

Rocklin, M., 2016: Dask: Parallel Computation with Blocked algorithms and Task Scheduling. Proceedings of the 14th Python in Science Conference (K. Huff and J. Bergstra, eds.), 130 – 136.

Managing your supervisor

Hannah Gough1 Comment

Written by: h.l.gough@pgr.reading.ac.uk

You’re going to be working with them for a while. Supervisors, like projects, are all unique and have their own ways of working. Lots of us have banded together to give tips and advice on how to ‘manage your supervisor’ and by that we mean make the road towards a PhD a little bit easier.

For those of you looking to start a PhD, getting the right match between you and a supervisor is key. PhDs are already stressful enough without a strained supervisor-student relationship.

Know how they work

Supervisors all work differently. Some will leave you to wander for a bit before drawing you back to the point, and others will provide a map of where you’re going. There’s no right or wrong but sometimes their methods can get frustrating when you start comparing supervisors.

Find out the best way to contact them. Some never reply to email and others are never in the office or dislike being disturbed. Figure out between you and your supervisor the best way of getting in contact.

Personal and work balance

Some supervisors are happy to talk about personal problems. Others aren’t. Again, neither option is right or wrong, but it’s something you have to be aware of.

Ask for things

A PhD is intended as a personal development training programme and not just for writing a thesis and publishing papers. Don’t be afraid to ask to do something different, such as environment-Yes, internships, field work and summer schools to name a few.

If you don’t ask, you don’t get.

Manage expectations

Saying yes to all the work they give you is only going to lead to disappointment for them and you. Be honest with the amount of work you can do, and say when you’re having a bad week. They’ll understand. Say when you’ve got enough on your plate already.

Know how long they take to read things, otherwise you’ll end up disappointed when the feedback you expected on a certain day doesn’t arrive.

Don’t expect them to be on email 24/7. Likewise, let them know that you’re not going to be checking emails at 3am either.

Know their style and expertise

Some come across more critical than others, some highlight the good as well as the bad. Their subject may make them biased on certain topics. Knowing their expertise allows you to tailor questions for them.

This is a lot more relevant to people with multiple supervisors, as often you can get two conflicting opinions and have no idea which one to accept. This happens, and it does teach you some diplomacy skills, but don’t go picking sides.

Get advice from other students

Chances are, other students will be supervised by your supervisor. Ask them for hints and tips of how they work. Ask about pitfalls to avoid and helpful tips. They might even have a manual on how to deal with them! There is a camaraderie between people who share the same supervisor!

If you’re still stuck and doing a PhD at Reading University, there’s an RRDP course by the graduate school called managing your supervisor. Definitely worth going to.