Oct 5-9, 2020
Virtual event on
Good Scientific Practices in EEG and MEG research
Good Research Practices: what makes a reliable M/EEG study?
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Know your own, and others’ biases while planning and conducting research – Manuel Mercier, Madison Elliott, Walter Sinnott-Armstrong
Current and upcoming pre-registration practice in the lab, and in the editorial system – Sophie Herbst, Roni Tibon, Pia Rotshtein
Anticipate to ensure the quality for your data and meet the standards – John Mosher, Katia Lehongre, Giovanni Mento, Emily Kappenmann
Know the strengths and limits of your methods – Karim Jerbi, Jean-Marc Lina, Alex Gramfort
A draft proposal and roundtable discussion (Guest session) – Anđela Šoškić, Suzy Styles, et al.
On the reproducibility of M/EEG research – Bertille Somon, Yuri Pavlov, Aina Puce
Learn about new tools for open collaboration – Guiomar Niso, Martina G. Vilas, Antonio Schettino
At your own risk: great tools don’t make good practices – Alexandre Gramfort, Arnaud Delorme, François Tadel, Robert Oostenveld
Power, reliability, and robustness – Maximilien Chaumon, Aaron Caldwell, Steven Luck, Guillaume Rousselet
Great software tools for improved research practices– Marijn Van Vliet, Laurens Krol, Andrea Brovelli
Taking a step back, and thinking about practices in the long run… – Daniele Schon, Yseult Hejja Brichard, David Poeppel
Monday 5 October
Human Factor (Chair: Maximilien Chaumon)
The inner practice of science and for science: the influence of cognitive biases on researchers and scientific production
In Wikipedia, a cognitive bias is defined as “a systematic pattern of deviation from rationality in judgment”. As any human being, researchers are prone to cognitive biases, which is a critical matter as these biases can lead to an “unsustainable” science. In this talk, and after a brief introduction on cognitive biases, we will see how they can influence our research for instance through perceptual distortion, illogical choice or misinterpretation. Next, we will envisage some remedies to counteract our inner trend for irrationality. Finally, together with the M.E.EG community we will discuss what can be done at a larger scale to reduce the impact of cognitive biases on scientific practices.
Some common fallacies
All humans make mistakes, and neuroscientists are no exception. We need to understand and watch out for common fallacies in reasoning in order to avoid them in neuroscience research, just as in everyday life. My talk will focus on a few fallacies that are common in some kinds of neuroscience research, including EEG, and illustrate these fallacies with real examples.
Pre-Registration (Chair: Antonio Schettino)
Preregistered reports in M/EEG research – a road map through the garden of forking paths?
M/EEG analyses pipelines combine a multitude of processing steps, such as filters, artifact removal, time-frequency transforms, etc., to be chosen by the experimenter. Given that it is impossible to test the independent contribution of each step to the results, novice and even expert neuroscientists are often left with the frustration to not know how strongly a given effect (or the absence thereof) depends on the choices made in their pipeline. Preregistration provides a potential remedy to this problem in that the pre-and post-processing steps are fixed before the study is conducted, and, importantly, expert feedback can be obtained early.
Based on recently obtained ’in-principle-acceptance’ for an EEG replication study, assessing the seminal finding of enhanced delta phase coherence by temporal predictions (Stefanics et al. 2010), I would like to discuss to which extent preregistration can foster replicable and robust EEG/MEG research, and help the community to devise less user-dependent pipelines.
Prereg posters: Presenting planned research in academic events
We recently proposed ‘prereg posters’—conference posters that present planned scientific projects—as a new form of preregistration. The presentation of planned research, before data are collected, allows presenters to receive feedback on their hypotheses, design and analyses from their colleagues, which is likely to improve the study. In turn, this can improve more formal preregistration, reducing the chances of subsequent deviation, and/or facilitate submission of the work as a Registered Report. In my talk, I will review data collected at the BNA2019 Festival of Neuroscience, where prereg posters were recently implemented. I will show preliminary evidence for the value of prereg posters in receiving constructive feedback, promoting open science and supporting early-career researchers. I will then discuss the outlook of prereg posters, particularly in the context of the current shift towards online academic events.
Live posters & virtual socials
Tuesday 6 October – Track 1
Two parallel tracks – you can jump from one to the other
Collaborative tools (Chair: Karim Jerbi)
14:00 (Paris) / 8:00 (NYC)
14:20 (Paris) / 8:20 (NYC)
Martina G. Vilas
The Turing Way: A guide to reproducible, ethical and collaborative research practices
Reproducible research is necessary to ensure that scientific output can be trusted and built upon in future work. But conducting reproducible research requires skills in data management, software development, version control, and continuous integration techniques, that are usually not taught or expected of academic researchers.
The Turing Way is an open-source, community-led handbook that supports this knowledge in an accessible and comprehensible form for everyone. Its moonshot goal is to make reproducible research “too easy not to do”. In addition to discussing different approaches for reproducibility, it also provides material on ethical practices in data science, inclusive collaborative work, and effective communication and management of research projects. The handbook has so far been collaboratively built with the help of more than 175 people from different disciplines and career stages within data research, who have contributed to the project’s online repository (https://github.com/alan-turing-institute/the-turing-way).
This talk will give an overview of The Turing Way book, project, and community, and will show how you can get involved in its development.
14:40 (Paris) / 8:40 (NYC)