Oct 5-9, 2020

First edition

Virtual event on

Good Scientific Practices in EEG and MEG research

Sessions

Good Research Practices: what makes a reliable M/EEG study?

 

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Human factors

Know your own, and others’ biases while planning and conducting research Manuel Mercier, Madison Elliott, Walter Sinnott-Armstrong

Pre-registration

Current and upcoming pre-registration practice in the lab, and in the editorial system Sophie Herbst, Roni Tibon, Pia Rotshtein

Data Collection

Anticipate to ensure the quality for your data and meet the standards John Mosher, Katia Lehongre, Giovanni Mento, Emily Kappenmann

Signal processing

Know the strengths and limits of your methods Karim Jerbi, Jean-Marc Lina, Alex Gramfort

A new Reporting Framework for EEG

 A draft proposal and roundtable discussion (Guest session) Anđela Šoškić, Suzy Styles, et al.

 

Reliability

On the reproducibility of M/EEG research – Bertille Somon, Yuri Pavlov, Aina Puce

Collaboration tools

Learn about new tools for open collaboration – Guiomar Niso, Martina G. Vilas, Antonio Schettino

Software (mis)uses

At your own risk: great tools don’t make good practices Alexandre Gramfort, Arnaud Delorme, François Tadel, Robert Oostenveld

Statistics

Power, reliability, and robustness – Maximilien Chaumon, Aaron Caldwell, Steven Luck, Guillaume Rousselet

Coded tools

Great software tools for improved research practicesMarijn Van Vliet, Laurens Krol, Andrea Brovelli

Beyond electrophysiology

Taking a step back, and thinking about practices in the long run… – Daniele Schon, Yseult Hejja Brichard, David Poeppel

Program

Monday 5 October

Time (UTC)

Speaker’s name

Talk Link

Human Factor (Chair: Maximilien Chaumon)

13:00

Manuel Mercier

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.

13:20

Madison Elliott

Title TBA

TBA

13:40

Break

13:50

Walter Sinnott-Armstrong

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.

14:15

Round Table

14:45

Break

Pre-Registration (Chair: Antonio Schettino)

15:00

Sophie Herbst

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.

15:20

Roni Tibon

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.

15:40

Break

15:50

Pia Rotshtein

Title TBA

TBA

16:15

Round Table

16:45

Live posters & virtual socials

Tuesday 6 October – Track 1

Two parallel tracks – you can jump from one to the other

Time

Speaker’s name

Talk Link

Collaborative tools (Chair: Karim Jerbi)

14:00 (Paris) / 8:00 (NYC)

Guiomar Niso

Title TBA

TBA

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)