Lab philosophy

We do science not papers. Nothing beats the feeling to have just discovered something new about the human mind. That feeling you get when knowing something that no-one else is aware of yet. Communicating this with others by writing a paper about it, but also via posters or talks, is a great and vitally important way to disseminate your discovery. However, bear in mind that scientific papers are a means and not a goal. We are scientists, not paper producers.

Learn how to code (with open software). Learning how to code makes you a better researcher. It trains you think in a logical way, a critical skill when tackling scientific questions. When you write code, it only makes sense that other people can reuse your code independently of their financial situation. Therefore, we prefer open access software whenever possible. We aim to use R for statistical analyses (see here if you’re an absolute beginner), PsychoPy for coding up experiments (perfect introduction here), and MNE-Pythonfor EEG analysis. For large analysis, such as hierarchical drift diffusion modeling, you can use the supercomputer.

Open Up. In the spirit of open science (if you haven’t read False Positive Psychology and the big replication project do so now!), we aim to make all experimental materials, raw data, analysis code (on GitHub and/or OSF) and the resulting manuscript (on BiorXiv) freely accessible. Knowing beforehand that you eventually will share this with the scientific community urges you to be a better programmer, and more generally a better scientist. This also implies your experiment and analysis pipelines should be properly documented, so that anyone can use your code and raw data to reproduce your findings while being able to understand how you conducted your research. This can be daunting at first (“what if I made a mistake in my code and everyone sees it?”), but it will help to build up your confidence and feel comfortable and proud about your work. Learning how to use version control using github, will be very rewarding once you start working on more than one project or when your computer crashes. Having your scripts out there also serves as a proof of your skills. To further facilitate this, it is recommendable to have a fixed data organization structure. An example that I know to be effective is a folder per project with subfolders experiment, raw data (never touch this folder), analysis scripts, figures, presentations, and manuscript.

Collaborate. The time of ‘one scientist knows it all’, is gone. Reach out to others who have the expertise you would like to acquire. Don’t hesitate to ask both your direct colleagues as well as leading experts in the field to help you out and collaborate with you. Most experienced researchers are very willing to help out young researchers. Talking to others about your research is one of the best way to advance your research. Go on twitter and follow researchers whose work you admire.

Embrace failure. Scientific papers report the end result of a long road often filled with bumps and holes. Seeing only the final result, the details of the challenging process left out, while you are struggling yourself, can be confronting and frustrating. Keep in mind that science is a slow and error-prone endeavor. Importantly, “failed” experiments are not failures – if the experiment was carried out properly a null result is equally informative. Did you spot a mistake in your programming/analysis/etc.? Use this as a learning process, and know that this happens far more often than you would think. As Karl Popper would say: “Be scrupulous in admitting your mistakes: you cannot learn from them if you never admit that you make them”.

Lab meetings. Learning to present is important (and the same goes for writing), in a lot of (scientific) jobs being able to provide clear and concise presentations is a highly valuable skill. With a well-presented poster or talk you can reach a wide public. Therefore, we organize practice sessions for every poster presentation or talk at a conference, so that you are always fully prepared to present. Moreover, during lab meetings you can broaden your horizon, learn new things and create a stronger and healthy lab culture. The organization of lab meetings is definitely not fixed, ideas for improvement are very welcome.

Be kind to others. Please be considerate of the whole spectrum of researchers. Do not judge scientific merit based on English proficiency and take into account that people have varying needs. One example is to use colorblind friendly color palettes, so that you do not miss out on ~5% of the population. Related to that, many readers will only/first skim your figures, so make sure you have clear figures with beautiful colors.

Be aware of your biases. As researchers with a background in psychology, we especially should be aware that humans are biased, e.g. with respect to gender and diversity. There’s probably not an easy fix to this, but being aware of our biases and trying to act on them is a necessary first step.

Don’t forget to live! One exciting part of academia is that you have a lot of freedom in deciding when you work. This comes at the cost of you also having to decide when not to work, which is equally important! We are all different, and finding the right rhythm is a learning process. Inspiration usually hits me after a weekend off, and reversely my brain stops working after a long night of work. Obviously, work also needs to get done. To keep your mental health, keep a good work-life balance!