It’s episode nine of my June Blogs and this time I want to write a companion piece to the last post, which was about how to start a project.
Like a lot of stuff I post on here, I’m not talking so much here about the intellectual process, but more the pragmatic decisions and steps to be taken to round off a piece of work – whether an undergraduate dissertation project, Masters, PhD or something bigger. So here I go, in no particular order, with….
Ten things to do at the end of a research project
1. Update your website / social media
Too many project websites get set up with great joy at the start of a project and then are fossilised in that perpetual state of optimism. Remember to get in there and change all the “we will”s to “we have”s. If you have a nice up to date webpage for the study then you can not only put all your outputs up on there, but also link to it in talks and on posters. It’s helpful to have a single place to collect together the different kinds of materials you create from the work you did, and this single place to find everything will increase the longevity and impact of the work.
2. Inform your participants
Everyone sets out with the best of intentions to let the individuals and organisations know what they did in their work. In fact, we often state explicitly in our recruitment materials that we will share feedback. But following through on this is hard when you are trying to write a dissertation, perhaps also publish journal articles, and apply for new courses, jobs or grants.
Still, remember that your work wouldn’t exist without the time that people invested in being your participants. It is quite literally the least we can do to feed back to them what we found and express our gratitude. Remember as well that those participants may well be asked to participate in research again in the future. If they have a positive experience in your project they will be much more well disposed to the next person that comes along. In this way, your work ad your relationship with your participants is part of a much bigger and wider research community.
When feeding back to participants, put yourself in their shoes – what do they want to know? Yes your significant results might be interesting to them. But also make sure you draw out what these results mean on a practical level. In doing this, be sensitive to the possibility that your participants will seek individual meaning, which is quite distinct from your group average comparisons. For example, if you found on average that people with blonde hair are more likely to be overweight, does that mean I should put my blonde son on a diet? NB: totally made-up and facetious example.
They might also be curious about the meaning of the tasks they did. You can tell them stuff like “you might remember we asked you to make designs by joining together red and white coloured blocks. This task tells us how good you are at using visuo-spatial skills – the skills we use to create and see patterns, and to navigate the world.” In other words, sharing information about your methods can be just as interesting as sharing results. And finally, don’t be afraid to share your personal success. If you’ve presented the work at a conference, got a good grade in your dissertation or have a new job – tell your participants. They will be glad to know that, thanks to their contribution, there’s one more lovely scientist in the world.
3. Share the practical implications
Don’t stop at just informing your participants. Think about the practical implications of your work – is there anyone who might benefit from using the insights you’ve gained to inform their decision-making? Even if your work isn’t published in a journal article, that doesn’t mean it isn’t useful for practitioners to read. For example in this undergraduate project supervised by a colleague the student managed to produce a really excellent infographic to inform clinical practitioners of things to think about when looking after autistic patients in hospital. One audience you might want to reach are the policy-makers – but remember that when we say “policy-makers” we don’t just mean politicians. A headteacher is a policymaker in their own school, choosing how to spend the school budget and what training to seek out for teachers. LIkewise a clinical service manager or an individual private practitioner both make policy to apply in their service.
4. Return any shared resources
if you’ve borrowed tests from your department library, make sure these are returned. Perhaps you also had a video camera, or dictaphone for your project? Make sure it is returned, ideally in the original box and failing that boxed or bagged up so all the pieces are together. Double-check that any SD cards and the device memory are all wiped. Make sure the charging cable and other bits and pieces are included. If you had any experiences with the kit – e.g. battery drains very fast – write a note for the next user and pop it in the box.
If anything you used had a password for access – maybe iPads or a department laptop – change this before returning to something generic and make sure a suitable person holds that password. Alternatively, if the device has been fully wiped of anything confidential maybe you could remove the password all together until the next user comes along?
5. Tidy, label and publish your data
Even the most well-intentioned individual will end up with a bit of a mess of data at the end of their project. You might have raw files for each participant, as well as a combined database. From this combined database I bet you’ve saved some versions with only a few variables for sharing with specific people, or maybe you have a copy with outliers removed or something else. Of course, excellent data management means a single “golden copy” but in reality this is rare unless your dataset is being compiled and held by a large consortium.
Even if you’ve managed to hold just one master copy of your data, is it transparently labelled? If you came back to it in five years’ time, would you know what all those variables were? or how they were measured? Even a really simple-seeming variable like age can be confusing later on (in years or in months??) and in other cases the coding needs to be made explict (is it 1 = autistic and 0 = non-autistic, or the other way around?)
So take an afternoon right now to label your data. First, go back to the raw data files and any interim files during processing and label them transparently and consistently. A good system is a folder for each participant, which contains identical files per person, personalised with their participant code. For example, in a folder called P001 I will find P001_ADOS.mov and P001_WASI.pdf and P001_parent-play.mov – and there are the same files in a folder for P002, P003 and so on. Alternatively, you might want to organise by data type (all the ADOS in one folder, all the WASIs in another) or have sub-folders for timepoints (P001 > Baseline, P001 > Outcome). Whatever you do, make it methodical, make it transparent, and make it consistent.
Next think about your processed data set – the variables you used in your analysis. Discard partial duplicates and settle on a single golden copy. While you’re at it, make sure you wipe the original data stored on any online platforms too – like Qualtrics or Survey Monkey. Label the content clearly and create a glossary to define each variable including what it measures conceptually (e.g. gender) and / or mathematically (e.g. the sum of response to items 1-7 on [this] survey). There’s an example of a really great published data set, with a lovely glossary and explanatory infromation made by my colleague Dr Maragret Laurie available here.
Once you’re done, publish your data if you possibly can. We’ve done this on a system hosted by University of Edinburgh called Datashare. But there are also public archives like the Open Science Framework or Figshare. Take as much time getting this right as you would publishing a journal article (though in this case you don’t have to worry about getting a desk reject). If you can’t publish it, then try to let people know the data exists by publishing metadata instead.
6. Compile and share a project archive
It would be heartbreaking if all that hard work tidying up your data went to waste. And it really might, if you quickly move on to new things, your ancient laptop dies, or your University IT account is shut down. So before all that happens, make sure your supervisor, line manager or someone with a long-term role at the institution has the full archive of your project. This should include:
- your golden copy data and raw data files – which should obviously never have left the University’s system – but still might be lost if your account expires and the associated filespace is wiped. Make sure your supervisor knows how long the data are permitted to be kept under the terms of your ethics approval.
- contact details for any recruitment gatekeepers and – if you have permission to keep it – your participants themselves.
- your final report, ideally as an editable word document as well as a final pdf
- any measures you used that are non-standard – e.g. a download of the full text of an online survey, a copy of an interview script, a standard operating procedure for a lab technique, a protocol for a video coding scheme.
- a complete set of ethics documents including your original application for ethics, the committee’s approval letter, and all the documents they approved – consent forms etc.
- any other outputs from the project, such as a conference poster, video of a talk etc.
7. Clear your drafts folder
Once you have yourdata tidy and published / archived, and all your final project documents too, it’s worth looking at the draft materials your produced during the project and perhaps deleting some of these. This is most important for partial / duplicate data – covered above – but you will also have versions of your report / dissertation, minutes of meetings and so on. These aren’t doing any harm but I tend to think that, in the interests of fostering good practice, it is worth clearing out these things at the end of the project. I know not everyone has such a fever for deletion as I do though – maybe you can’t bear to dispose of all that hard work, even if you doubt it will be reviewed ever again. In which case, fine, but do check that no contact details or other identifiable information is tucked away in there, and do try to organise your project archive tidily so you can navigate it again one day if you need to.
8. Update your CV
This is a moment to think about the skills you have developed during your project, so don’t just put your latest degree result on your CV – add your project title and a few bullet points that show what abilities you have that might be relevant for future employers. Make sure these are as transferable as possible, especially if you’re looking for a job beyond academia. So rarther than “presentation at such-and-such conference” how about “presentations to an expert audience”. Rather than “assessments with the WASI and Emebedded Figures Test” you could say “face-to-face cognitive assessments and adherence to standard protocols”
9. Plan your publication strategy
This probably won’t need mentioning as most people with publishable work will be well on top of this – especially if they want to continue in academia. However I think a couple of things are worth saying…
First, do think critically about what sections of your work really need publishing. Yes at early stages of your career you may feel a desperate need to populate your CV with papers. But I have seen countless postdocs agonise over unpublished thesis content for years post-PhD and really do wonder if all the stress and time is worth it. Especially if you’re chasing a busy former supervisor for comments, working on a new busy postdoc role and writing grants for the next one at the same time. Especially if you start a famiy and / or there’s a pandemic while you’re doing those things! So do examine your work and think about what needs to go to a journal. if you can’t bear to let things go, at least come up with a priority order for what you want to get out there. Remember that you can also post your thesis chapters as pre-prints on a service like PsyArxiv or submit work to open journals which guarantee publication if basic methodological standards are met. The treadmill of submission, revision, rejection, reformat, submission, rejection, reformat, submission, revision, revision, rejection, reformat… doesn’t have to happen.
Second, do think consciously about publications as a strategy. Decisions might include:
- what journals should each piece of work go to? do you need to “aim high” for every output or could some have a less agoninsing publication journey if you select a journal that has a more niche remit – perhaps fewer citations but also a better home for your work?
- who are the lead co-authors? I find the most efficient way to put together papers is for the first author – normally the student or postdoc who did the bulk of the hard work – to pair with a senior author and produce a first draft they are both really happy with. These two can then share that draft with a wider author pool for comments and adjustments which should ideally be fairly modest and close to submission. Working this way, for example, a PhD student with two supervisors might pair with a different one for two different papers and this way minimise the problem of busy supervisor bottlenecks preventing completion of a manuscript.
Wow – this was another blog I expected to be super quick but which actually exploded under my fingertips! After all your hard work, and all my typing, we both deserve a celebration!
At the end of your project, make sure you take a moment to relfect on everything you learned and to celebrate your achievements. Even if you didn’t get the grade you wanted, you will have developed a huge repertoire of new skills and these will stand you in good stead in the future. Do also send a goodbye email to your supervisor – they will love to hear from you and to have the chance to thank you for working with them. Especially on smaller projects, maybe as an undergrad or a visiting student, sometimes we forget to mark the end of a project and this is sad. That said, don’t feel the need to buy a gift for your supervisor – they were doing their job which, despite complaining about it, I’m guessing they probably absolutely love! I certainly do…