These are the slides for an informal talk that I presented on 2 December 2022 at the DTU in Kopenhagen, Denmark.

1. Developing and sharing tools for
bioelectromagnetic research
Robert Oostenveld
Donders Institute, Radboud University, Nijmegen, NL
NatMEG, Karolinska Institute, Stockholm, SE

2. Short history of live, and everything…
2002 completed PhD thesis
2002 start Donders Centre for Cognitive Neuroimaging
installation of MEG and MRI scanners
2003 internal sharing and coordination of code
with Pascal Fries, Ole Jensen, Jan-Mathijs Schoffelen,
Markus Bauer, others
2004 sharing of code with external co-workers,
FieldTrip was born
2003 collaborating and code sharing with EEGLAB
2005 collaborating and code sharing with SPM
2011 FieldTrip reference paper

3. Short history of live, and everything…
2004 first scientific results appearing from Donders
2002 yearly “toolkit” training events
2012 Stapel affaire (scientific misconduct)
2016 Donders Repository for research data
2016 first version of BIDS, followed by MEG, EEG, iEEG, PET,
NIRS, …
2021 Open Brain Consent project and paper

4. My tools for Open Science
Code for data analysis
FieldTrip
Methods for data sharing
Donders Repository
Ethics/Legal consent
BIDS to organize the data
They allow me to do my research and have others
replicate it and build on it

5. Issues that we are jointly facing
Improving quality, efficiency and impact
Reproducibility crisis
Increasing complexity of research
Gap between academic training and professional career
Open Science can contribute some solutions

6. Lack of trust - in society
http://harrieverbon.blogspot.nl/2012/11/diederik-stapel-werd-ook-betaald-door.html

7. Lack of trust – among scientists
Open Science Collaboration, Science (2015). DOI: 10.1126/science.aac4716

8. Replication crisis
Incentive structure results in focus on sexy results
Publication bias results in more papers with positive than
negative results being published and read
Trouble in the lab, The Economist (2013)

9. Incentives in academia
Your career will benefit from
Many publications
High-impact publications
Spectacular results
This may result in undesired
behavior
P-hacking
Harking
Survival of the fittest
promotes bad science
(Smaldino & McElreath, 2016)

10. The EU's open science policy
“Open science is a policy priority for the European Commission
and the standard method of working under its research and
innovation funding programmes as it improves the quality,
efficiency and responsiveness of research.
When researchers share knowledge and data as early as
possible in the research process with all relevant actors it
helps diffuse the latest knowledge.
And when partners from across academia, industry, public
authorities and citizen groups are invited to participate in
the research and innovation process, creativity and trust in
science increases.”

11. Open Science
Open educational resources
Open access publications
Open peer review
Open methodology
Pre-registration
Open source
Open hardware
Open data
Markus Neuschäfer; https://www.slideshare.net/mneuschaefer/1504-open-knowledgefolien

12. Why is the transition to Open Science so hard?
Doing science is already hard as it is.
Academia is conservative, and slow to change.
We don’t learn from each other.
We read papers and see presentations:
We review and comment during the preparation of presentations
We review and comment during the preparation of manuscripts
We do not review or comment on code, data and procedures.

13. Improving scientific procedures
Doing an EEG/MEG study requires a good research question
Theoretical underpinning
Knowledge of relevant literature
Preregistration
Acquisition of data
Lab setup
Ethics
Data Management Plan
Design and implement your analysis
how to start with new (pilot) analysis pipelines
how to scale these to publication-quality group analysis
Handling of work-in-progress scripts, data, and results
Planning for and publicly sharing results, data and analysis details
Open Data, Open Source code, and Open Access manuscript

14. Transferrable skills for future career
Students are initially trained to use small computers
but expected to do computations on big data
Note: “big data” is complex data, “large data” is large in size but not per see complex

15. Sharing of analysis details (code)
Manage versions of your analysis scripts
Github, Gitlab, Bitbucket
Backup and share between computers
Collaborate and review
Also used for FieldTrip development
Toolbox code improvements
Website

16. Sharing of project plan and details
Many considerations and decisions are made during the
course of a project.
What to do, what not to do? What data to include, what data
not to include?
Electronic lab notebooks or platforms like the Open Science
Framework (osf.io) can be used for documentation.

17. Research data management
Publicly funded data should be as open as possible,
but as closed as necessary
Some interesting research data models
Allen Institute for Brain Science publishes all data
Astrophysics, shared infrastructure of large telescopes
You need a plan to manage research data
describe the complexity
describe the infrastructure
describe the procedures

18. Academic skills to acquire
Critical thinking and analytical skills
Good research practices
MATLAB or Python coding and code management
GitHub or other code versioning/collaborating
Data management, e.g. BIDS, ethics, legal
Where to share secondary results, e.g. Zenodo and OSF
Being able to learn from interaction with others
and from other disciplines

19. Summary
Improving quality, efficiency and impact
Reproducibility crisis
Increasing complexity of research
Gap between academic training and professional career
Open Science can contribute some solutions