Category: Good Research Practice

1. A detailed explanation of why you did everything.
2. The method you used to collect the data in as much detail as possible.
3. Your results with all the statistics.
4. What you concluded from the work you have done.
5. What are your thoughts for further work.
6. If you have any ideas for the long term direction of the project you could also include this.

The most important part to include is the methods you used and any results that went wrong. It will of course be embarrassing if you have made mistakes, but THAT IS WHAT THESE MEETINGS ARE FOR. The earlier you learn why things are going wrong the earlier you can put them right.

It is a mistake to think you can make these corrections on your own. It is human nature to bias our opinion of our work to protect ourselves from the depressing realisation that we are bad at things. Other people will spot your mistakes more easily, so show them.

1. Make sure you understand why you used the stats in your presentation.
2. Make the presentations look good. Don’t waste time spending hours changing fonts, but remember that a lot of what your supervisor thinks of you will be based on these meetings. They don’t see the hours you spend working out of hours. Make sure your data are well organised and coherent.
3. Label the graph axes thoroughly. Don’t use nondescript terms like increase or change, but describe what is increased or changed within the axis. If the description is too long, add a paragraph below the graph to show the explanation.
4. Show procedures that haven’t worked. There is little to learn from things that worked well, but where they failed there is probably a reason, and your supervisor or someone else in the group may know it.
5. Meet with your supervisor as regularly as they allow. If they are too busy or you don’t get on with them, then find someone else who will chat to you about your work.
6. When meeting one on one, have a plan of each point you need to raise pre-written on a notepad so that you can tick them off as they are discussed.

I used to hate presenting at lab meetings. As a result, I put little effort into making the data look presentable. The frequent and impenitent lack of stats or labelled axes earned me some harsh words from my supervisor. It took me too long to realise that just because these things were unimportant to me, didn’t mean they were to him.

I also only showed the data that I thought were interesting, and left off the data that didn’t work. Not only did my very brief presentations not impress anyone, but the experiments that were failing kept failing and I was forced to look for explanations elsewhere. When I finally showed the failed results, my supervisor and the rest of the lab made several very good suggestions that I tried.

It still didn’t work, but that’s science. At least I got to write that I tried those things in my thesis, which saved me from further scrutiny in my viva.

• Time allowance is consistently underestimated. Always allow yourself longer to do a task than you think you will need.
• People doing animal studies might be told they need 12 animals to make the study statistically robust. Using 12 would then fail to account for what happens if one of them dies, or if the variation is much greater than expected.
• Human studies should always follow the same principle. People can drop out or never turn up.
• If you have to deal with costs, then leave space in your budget for unexpected problems. Science is notoriously unpredictable.
• When using expensive (or even cheap) reagents, students often use just enough of it to completely screw everything up. This is an entirely false economy, as you will find out when you have to do the experiment again.
• When you are doing things in bulk make sure to add extra reagent or your last few samples might not receive the same volume of solution.

1. Take the time to actually calculate how much of something you will need and then add extra.
2. Ethical and safety approval is something that frequently takes far longer than students think. Make sure you allow for this in your experimental plan.

When you’ve just spent £500-700 on a tiny tube containing 3-4μl of liquid, your immediate inclination is to use as little of it as possible and make it last. I’ve done this over and over… and over and over. Then I probably went home and ruined dinner by not adding enough oil to the pan.

I would add just enough liquid to cover my slides so as not to waste reagents. This was frequently a disaster. The surface tension broke, the liquid spilled, or the slide dried still untreated. I might as well have poured that expensive reagent down the drain.

Secondly, I also frequently forgot that pipettes are not magic calculation devices. They carry a degree of error, and frequently take up more liquid then they are supposed to. Not a lot, but when I had 20 samples all receiving reagent from the same volume, if I hadn’t made enough for 22 there wasn’t always enough to cover the last sample equally.

• Don’t completely ignore them and do your own work. If they don’t like your ideas then discuss the scientific merits with them further. If they have questions, answer them, and if they find problems, find ways around them.
• If you don’t get along with your supervisor then that’s fine. Not everyone can get along. But that doesn’t mean you should deliberately antagonize them. Try and be as polite as possible, and when possible mend fences.
• Don’t form collaborations with other groups behind the back of your supervisor. This may embarrass them and they may not react well when they find out. Tell them what you are doing and what relevance it has to your project. If it doesn’t have any relevance, then you probably shouldn’t be doing it. Also state that you are doing it on the side, not at the cost of work they want you to do.
• If you are working hard out of hours, send them emails with updates to let them know. Otherwise you might be unpleasantly surprised by their impression of you.

1. Remember how easy it is to be biased towards your own work. Don’t just think about how good the data will be if they show exactly what you want them to show. Think about what else they might show, and whether that is still worth knowing.
2. Think whether you are doing the procedures the best way, and are you using the most precise measurement of the parameter you are testing.
3. Read literature in agreement and disagreement with your hypothesis, and consider the implications of the latter.

I was a big one for designing my own experiments. I was lucky enough to be passionate about what I was doing and my interest spawned ideas. In my head, all of them were ground breaking, but rarely did they survive a trip to my supervisor.

He was not being cruel; he had just spotted things about them that I hadn’t. Objectivity and experience allowed him to see the flaws in my work that I could not.

Whenever I tried to bypass his approval and run the experiments, I was always disappointed with my results. They all contained some flaw that I had not anticipated, or suffered from bad planning. I could have saved a lot of time and lab money by not bothering.

Others I have known have compounded this mistake by adding their own data unapproved to their thesis, and vehemently defending it in their viva. This almost led to failing their PhD.

Apply objectivity to your own work as you would to other people’s. Respect your supervisor and listen to their advice. It makes it all the more satisfying when they like your ideas.