Writing a research proposal – first steps

Science takes a lot of hard work, which can occasionally be a little repetitive and even uninspiring. At other times, it is an incredibly creative process, which requires a lot of insight into seeing how pieces of information are connected and what the bigger picture might look like.

As I mentioned before, I’ve recently been thinking a lot about post-PhD options and trying to come up with ideas for a possible project proposal. Although reading has been immensely useful for seeing what has been done already in my subject of interest, it’s been a lot trickier to identify potential gaps in the literature. This kind of creative idea generation is difficult as it can’t really be forced and just takes time and perhaps some luck. I’m reminded of the early days of my PhD, when I was trying to decide on specific analyses I could do within my chosen topic.

So, how exactly are you supposed to come up with a brilliant proposal idea? Although I don’t really know the answer to that, here is my attempt at muddling through and having a go at it.

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The first step of idea generation is getting something, anything down on paper, i.e. doing some brainstorming. A reasonable way to begin could be by jotting down any random ideas that come to mind and trying to think about possible gaps (no matter how frivolous or vague) regarding the existing literature in a fairly narrow topic of interest. It might also be useful to draw some diagrams. It sounds simple enough, but the longer you stare at an empty white page, the clearer it becomes that starting from scratch is daunting. I began by writing a literature summary of any studies that were relevant to an interesting observation I’ve noticed. This was a good starting point but I felt like I spent hours staring at my computer screen, trying (and most days, failing) to think of an insightful or clever way of exploring this observation further.

Once you do have some vague ideas, it’s useful to share them with colleagues, supervisors or even family and friends (particularly if they are academically-inclined) to get some feedback and have a discussion. It’s really easy to feel like you are going around in circles thinking about an idea and so having a general chat with someone else can give you new insights. If they don’t understand the connections you are trying to make, this might suggest that you need to think of the issues in simpler terms or take a step or two back. The media and popular beliefs paint a picture of science being about grand discoveries and great leaps in knowledge. In reality, we know that science is a cumulative process, typically involving many much smaller steps towards increasing our knowledge and understanding.

After some initial brainstorming and informal chats, a possible helpful next step is to do a short presentation and have a longer discussion with several supervisors/colleagues. Although this may feel slightly daunting to begin with, in my experience the prospect of doing a presentation is good motivation for transforming random half-formed ideas and jotted-down thoughts into a more coherent whole. Within the first 3 months of starting my PhD, I had to give a couple of departmental talks (the first on a research paper relevant to my PhD topic for a journal club and a second talk introducing my topic and research plans). I spent the first few months of my PhD doing little other than reading papers and preparing these 2 talks. The talks gave me a target for finalising my aims and the feedback was really useful.

The feedback from my more recent talk about my fellowship proposal ideas has similarly helped to steer me in the right direction. I’m now at the no-less-difficult stage of formulating specific aims and hypotheses and trying to design a method to address them. I have a long way to go with my fellowship proposal and I am conscious that all this effort might not even result in any funding, as the schemes are all extremely competitive. Part of my thinking was always that I would just try my best with this and learn something from the process.

Getting your paper through peer review

Whether you like it or not, productivity in academia/science is judged (at least in part) based on your publication rate. This focus on quantity is associated with various problems – see for example “Problem 4: Pressure to publish” of Chris Chamber’s recent post. Although the goal of doing science should not be the publications themselves, they remain an important method of disseminating our results and ideas to other researchers and the public. Learning how to write a concise, informative, thorough, accurate and all-round good research paper is clearly an important skill to acquire during your PhD, particularly if you hope to continue your academic career.

However, learning how to get your paper through the peer review process is also a skill that you need to acquire. The quality of your writing and your ability to include important details in your manuscript (i.e. the content) are of course directly linked to this but there are other important things too. I’m glad that the attitude of my supervisors and more generally, in my department, is that I should be publishing my PhD work when ready, as this gives me targets for writing up my thesis. Being involved in analyses and manuscripts outside of my core PhD has given me additional experience in navigating the peer review process.

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Step 1: Choosing a journal

Once you are happy with your analysis and have started writing a manuscript, you need to decide on a target journal for your work. I have written about this before and I have to agree with my previous thoughts on the matter: it just takes experience and time to get a sense of what journals publish what kinds of papers, in terms of their specific focus etc. When I’m reading papers, I tend to ignore what journal they came from, though sometimes I make a conscious effort to check this. Journal homepages frequently have a little blurb about what sorts of papers they will publish and scanning through the titles/abstracts of some of their recent issues can help. There are a couple of online tools which allow you to paste your abstract in and then scan it for keywords to give you some suggestions of appropriate journals. I’ve also tended to chat to my supervisors to get their more experienced thoughts on the matter.

Step 2: Choosing reviewers

Perhaps my least favourite part of submitting a paper is the need to identify potential reviewers. Some journals even require six different reviewers. How do you know who to suggest? Ideally, you want to identify people who have a good understanding of the background to your work and the methods you have used, are fair and have a good reputation and don’t have a clear conflict of interest. For my first few papers I relied heavily on my supervisors to suggest people based on their research interests and reputations. I kept a list of these suggestions and have re-used names for other submissions, where appropriate. Although in cases where my paper was rejected, unsurprisingly, I’ve tried to come up with alternatives.

I think the main way of identifying potential appropriate peer reviewers is by checking the author lists of the papers that are most closely linked to your own work. I’ve also found that going to seminars, talks and conferences is also useful. Seeing somebody present their work, especially if it is relevant to yours, means you are much more likely to remember their name. If you get the opportunity to speak to them, for example if you are lucky enough for them to visit your poster presentation at a conference, you can even get a sense of what kind of person they are and what they think about your work. I think this may also be particularly helpful for trying to identify potential PhD thesis examiners.

One other thing to bear in mind is that the anonymity of the peer review process means you are unlikely to find out which of the people you suggested did end up reviewing your work and which of them were the ones who gave the positive and useful comments or indeed, the less polite ones.

Dealing with rejection – back to step 1

Even after writing as a good a paper as you can, having it internally reviewed by your supervisors and co-authors, proof-read by your colleagues and settling on a journal and possible reviewers, the chances of having your publication rejected can feel depressingly high. It happens to the best of academics, sometimes for the wrong reasons (e.g. null results from a well-powered good study design) and sometimes because of bad luck in having reviewers who have misunderstood your work and not given you a chance to clarify it. I think that being able to cope  with the occasional rejection and carry on without letting it get to you personally is an important thing to learn too.

Step 3: Responding to reviewers’ comments

It’s important to bear in mind that (almost) regardless of what the comments are, being asked to do revisions is good news! I don’t think I’ve ever heard of a paper being accepted for publication without at least some minor revisions. More frequently, major revisions are called for, oftentimes multiple rounds of these. Whether it is the anonymity of peer review or tactlessness to blame, reviewers’ comments can sometimes be a little upsetting in their negativity. For some examples of particularly harsh comments, see FemaleScienceProfessor’s recent “Fake Review Contest” blog posts. Alternatively, for some slightly more humorous examples, see this Storify of the recent #SixWordPeerReview Twitter meme.

In an ideal world, the reviewers’ comments would all be very insightful, sensible and constructive, with a view to improving the manuscript. Although many comments will be quite reasonable, there are a few things to learn about writing your response to them. What should you do if you disagree on a certain point or if the reviewer is asking for something that you had actually already mentioned? I’ve been told by colleagues that if a reviewer is asking for something that is already there, you might need to make this item clearer and more prominent (especially if it’s hidden away in supplementary text for instance). As for disagreeing, if you have a genuine scientific reason, you should probably defend your work and the decisions you made, although perhaps also amend your manuscript to discuss the issue further.

Another question concerns the tone of your response. Should you be excessively polite, thanking the reviewers for their useful comments multiple times, or just get straight to the point of addressing them? You don’t want to come across as abrupt or rude. Generally speaking, you probably need to be professional and polite, but assertive if you have solid reasons backing up how you performed your experiment or chose to present the results. It can be quite useful to look at how other people write their responses to reviewers, which you can do by looking at the “peer review history” of articles published at PeerJ.

Having a little bit of experience with how peer review works may help you anticipate the sorts of comments that you might get. Being a reviewer yourself (something I have only a little experience of so far) and discussing research articles during a journal club can also highlight what kinds of things will make a good publishable research article.

One step ahead of the game

It’s that time of year again, when my friends and peers are busily editing, formatting, referencing and proofreading their final PhD thesis drafts. Inevitably, this has brought the goal of my PhD to the forefront of my mind. But more so than at the end of my first year, this has come with an additional sense of impending panic about my post-PhD plans.

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I’ve written before about the stressful consequences of not thinking about and planning my career soon enough. So I know all too well that it’s never too early to start preparations for the next step. Naturally, these preparations involve researching available options.

So what are they? These generally split into options that involve staying in academia and research vs. options that do not. Much has been said recently about the general difficulties facing PhD graduates in terms of leaving academia, even though statistically speaking, the majority of us are going to do just that. As a psychology postgraduate, I could:

  • Leave research and compete against the 100s of other graduates to try to get a place on a training course to specialise as a clinical, educational or other psychologist
  • Leave psychology for a vaguely related but new subject (e.g. medicine, social work or teaching)
  • Leave both psychology and research all together and get into science communication or join some graduate scheme or something entirely different

Yes, those are all viable options… but not ones I am keen on. I want to stay in research because I love it and this is what I really want to do. According to my “individual development plan”, my skills, interests and values are most compatible with being “Research staff in a research-intensive institution”. So, with that in mind, what should the next step be?

  1. Becoming a lecturer in the hope that in between preparing and delivering lectures, marking coursework, supervising and mentoring students and doing admin work, there will be time to squeeze in a bit of research
  2. Competing for a coveted but short-lived post-doc position on somebody else’s grant
  3. Failing getting a post-doc position, trying for a research assistant post (for which you wouldn’t need a PhD). Judging by the difficulty of getting one of these without a PhD, I’m pretty certain a lot of PhD students end up doing this.
  4. Applying for your own grant money via one of the supremely competitive “early career” fellowship schemes

Every option is bound to be competitive and all options (except perhaps the lecturer route) are going to be short-term (1-4 years from what I’ve gathered) before you need to try for a new post. Many of the options may also involve moving to a new university/city, which can come with all sorts of complications.

At the moment, it is far too early for me to think about the first 3 of these options as positions will only be advertised at most a couple of months in advance. I have just over a year to go before my funding runs out. The fellowship route on the other hand would involve about a year-long application process, so now would be the time to start considering this option.

I’ve recently been to a few talks and workshops on fellowships and have been looking into specific schemes that offer something I might be eligible for. Although it sounds like a really exciting and appealing option, it also looks pretty tough and competitive.

One of the trickiest parts is coming up with a brand new, exciting and viable research idea. I’ve already had to come up with an idea for my undergraduate dissertation and then an idea for my PhD, followed by several more specific ideas for my PhD research chapters. The trouble is trying to find time to work on developing a new project idea (fellowship), while still working on the last one, which is still far from done (PhD).

Perhaps that in itself is a useful skill I should practice, given that that is what the cycle of grant-writing appears to be like. From my limited experience of seeing supervisors and professors writing grants, there is only a short grace period in between successfully receiving grant funding and beginning the next grant application, while the current work is being done. Well, it can’t hurt (too much) to try, right?

PhD productivity vs. having a life – both please!

Scientific research is pretty competitive, especially when it comes to securing funding. As such, our work is never truly done and to-do lists can feel endless. These two things frequently result in researchers working extremely long hours. A fascinating article on this subject was making the rounds on Twitter last week. In the post, a tenure track researcher at Harvard University mentions that some people believe that success in academia can only be achieved by working 80 hours/week. She points out that this “would mean ~11 hour work days all 7 days of the week. That’s crazy, and *completely* unreasonable”. I agree wholeheartedly.

This workaholic mentality is quite pervasive though, both in academia and beyond. PhD comics frequently illustrate the absurd idea that PhD students are expected by their supervisors to dedicate all their time to their research (see a recent comic here). Although the comics are fortunately an exaggeration, many PhD students do feel the pressure to work long hours to compete for the precious few post doc positions out there. I’m reminded of this excellent article by Scicurious from a couple years ago, commenting on the many problems with fostering this kind of attitude in early career researchers.

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Surely working excessively long hours is counter-productive. After a certain amount of hard work and mental effort, one’s ability to concentrate and the quality of work decreases dramatically. Our brains need adequate sleep and nutrition in order to perform optimally. Similarly, relaxing, spending time with family and friends, exercising and doing hobbies are necessary to stay mentally healthy and combat stress. A quick route to an unhealthy and overly stressful lifestyle involves trying to get by on caffeine rather than getting enough sleep (see this fascinating article), repeatedly missing out on social events and skipping meals or eating take-away/microwave meals.

There is no question that we need a work-life balance. The question is, can you succeed in science by working “only” 40-45 hours a week? Where I work, most people go home by 6pm and do not lament or brag about working at weekends. I occasionally receive work emails that have been sent on weekends or late in the evening, suggesting that at least some of my colleagues work from home outside of core office hours. I certainly check and reply to emails/do other work from home on occasion too. However, I strongly doubt that anyone in my research group regularly works more than 50 hours/week. And yet, as a group we seem to be reasonably scientifically productive, healthy and happy.

Just to clarify, I completely understand the occasional need to work longer hours in order to get something important done or to meet a deadline. Friends approaching their PhD thesis submission deadlines definitely put in more hours of work a week than average. Similarly, professors writing grants clearly also work longer hours in the weeks before submission. These are understandable exceptions though and they happen for a finite time period.

Perhaps I am really lucky to have found myself in such a healthy work environment or maybe I am incredibly naïve and underestimate how long my colleagues work. Either way, I would much rather work efficiently for 40 hours a week and take the time to recharge my batteries. There are plenty of technological tricks that can increase productivity and cut the length of time tasks take. For example, most statistical packages (Stata, R, SPSS etc.) allow one to use do-files/scripts/syntax to easily run, annotate and repeat analyses. Referencing programs (Endnote, Papers for Windows, Zotero or Mendeley) can cut out days of work, particularly when you need to re-do references after having an article rejected and preparing it to re-submit somewhere else. Microsoft Word has many built-in options that make working with long word documents a breeze (e.g. formatting styles, automatic table of contents). Learning how to use software fully and efficiently (i.e. understanding all the quirks and options) is an investment of time that will pay out exponentially.

In my (as yet limited) experience, I fail to understand how working 50+ hours a week can lead to increased scientific productivity that is worth the cost to one’s physical and mental well-being.


Digital Spring Cleaning

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I cannot stress how important backing up files is. It’s one of those things that many people learn the hard way. I’ve lost enough bits of work over the years of doing school homework and undergraduate coursework on computers that I now have a slight obsession with saving my work every couple of minutes and also regularly copying my files into several different places. I still occasionally lose a bit of work, but mostly through unpreventable and sudden network/computer crashes. I have a serious fear of and aversion to this happening and use creative damage control options (once I even resorted to taking a photo of my frozen computer screen and using it to re-write a paragraph of work after restarting the computer).

I would much rather prevent the loss of any data, if at all possible. Some of my regular preventative strategies for minimising chances of losing work are:

  1. At the end of every day and sometimes throughout the day too, I will back up the most important files I have been working on by copying them into my dropbox folder.
  2. I tend to have a subfolder labelled ‘old drafts’ in any folder with ongoing work (e.g. paper I am writing, talk I am preparing or database I am using for analyses). This is just in case I accidentally or deliberately delete something I later decide I need.
  3. I also write a note to myself in my calendar to back-up everything (to a different drive, preferably more than one) about every 3 months.

I’m sure this all sounds great and hyper-organised but it is not a full-proof system. To begin with, I have a ridiculous amount of duplicated files all over the place. I regularly use lots of different hard drives (2 on my work computer, 3 networked drives I can access from any university computer, 2 drives on my laptop and one external hard disk) and about 5 memory sticks scattered about the place. Also, in addition to uploading files to dropbox, I still sometimes email things to myself (old habits die hard). It can occasionally be tricky to find something, especially if it’s something I haven’t worked on for a while.

I have gradually learned to label documents, folders and subfolders in a sensible and informative way but this just means that some files/folders have rather lengthy names. Sometimes I need to use the search tool in Microsoft to find where I squirreled away a particular document.

One helpful thing which goes some way towards mitigating the chaos is that I have one primary drive I use, where I keep all of my up-to-date files and I put back-up files in the various other places. Lately I’ve been trying to do some tidying up to sort through the mess. However, deleting files is a slightly frightening thing to do and involves double checking things really are duplicated or massively out of date. Generally, I just leave things be as I have loads of extra storage space anyway.

I have a similar approach to my emails. I like to keep my inbox relatively clear so as soon as I have read and replied to or otherwise dealt with an email, I file it away. I have a labyrinth of folders and sub-folders for my emails. I only wish our email client at work was as sensible as Googlemail so I could label/file emails under more than one category. I do tend to have to use the search tool quite regularly, so obviously my filing system isn’t optimal.

If anyone has any tips on keeping your digital stuff organised and backed-up, do share.

A Framework for Mental Health Research (RDoC)

Although the Research Domain Criteria project (RDoC)  is not particularly new (the description of it on the NIMH website is dated June 2011 and it’s been on my reading list for at least a year), there has been a lot of attention drawn to it recently. This is partly because the DSM-5, the new psychiatric diagnostic handbook, is due to be published on May 18th, prompting Thomas Insel, director of the National Institute for Mental Health (NIMH), to recently write about the necessary next steps in mental health research (see here and here). I’m sure many others (for example The Neurocritic) have written about this recently. Having finally gotten around to reading about RDoC on the NIMH website  myself, I wanted to briefly summarise what it is and why it’s brilliant. If you haven’t heard of RDoC or keep meaning to look it up, this is for you.

The RDoC project is a framework for thinking about and researching all aspects of human psychopathology/mental health, without confining the research to existing diagnostic labels. The DSM-5 is the best thing we currently have for the purpose of clinical assessments and diagnosis, in the hope of trying to treat and improve the lives of people with mental health difficulties. But research suggests that it isn’t good enough.

In reality, mental health conditions overlap greatly, both in terms of clinical presentation, associated features (e.g. cognitive difficulties) and in terms of apparently non-specific risk factors (e.g. many genetic variants have now been shown to play a role in more than one condition (e.g. schizophrenia, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), all previously thought to be quite distinct conditions)). Even within a diagnostic category, there is a lot of variability in severity and also, you don’t need a diagnosis to have problems; symptoms below diagnostic thresholds often cause difficulties for affected people. It has become clear that mental health problems are not binary. Instead, there appears to be a continuous distribution of mental health difficulties, ranging from none to very severe.

The RDoC framework is about cutting across these diagnostic labels and looking at the underlying dimensions of behaviours and measures of neurobiology. The idea is that recruiting participants to a study on mental health based on their diagnoses and then trying to determine how they differ from “healthy” controls in order to inform research on diagnoses, is actually a little circular. The alternative approach, suggested by the RDoC framework, is to recruit participants with a range of related problems (e.g. all types of mood disorders) and look within this group.

RDoC

(Example of the 2 main dimensions of the RDoC Framework)

The framework divides up mental health into a list of constructs (such as ‘Responses to potential harm (Anxiety)’, ‘Reward Learning’, ‘Cognitive Control’ or ‘Social Communication’) within more general domains (e.g. Cognitive Systems). It then divides research approaches into units of analysis (e.g. Genes, Molecules, Cells, Observed Behaviour etc.). Two other dimensions are ‘Developmental Aspects’ (how these constructs change over time) and ‘Environmental Aspects’ (how the environment affects and interacts with the constructs).

The hope is that considering mental health in terms of these dimensions rather than diagnoses, will serve as a research framework for improving our understanding of mental health and creating better diagnostic categories for the future. This framework seems to me a much more valuable way of doing research in this area. It reminds me of a great blog post by Dorothy Bishop from 2010, in which she argued that neurodevelopmental problems should be considered on a number of developmental dimensions, rather than as discrete clusters of difficulties (i.e. diagnostic categories). I was very inspired by this way of thinking when I first started my PhD and so I think it’s great to see that the NIMH is encouraging researchers in mental health to adopt this way of thinking.