Our ambition is to be as transparent about our scientific process and outputs as possible. We embrace the practices that are often placed under the umbrella of Open Science. For us, open science means supporting good research practices, improving reproducibility of research findings, making our outputs (e.g., articles, code) accessible for all, and aiming for FAIR (Findable, Accessible, Interoperable and Reusable) research data. On this page you can find some examples of how we put this in practice.
We aim for 100% open access to our written outputs (e.g., articles), either via the gold (journals) or the green route (self archiving at institutional repositories). We also publish non-peer reviewed preprints to increase the speed and impact of our research, and to allow for early feedback from the scientific community.
Open Science Framework:
We have an OSF page which we use to store project documents on.
Preprints of ongoing submissions can be found here:
Green, K., Becht, A., van de Groep, S., van der Cruijsen, R., Sweijen, S., & Crone, E.A. (2021).Adolescents’ Social Environment and Executive Functions Predict Long-term Mental Health and Feelings of Future Uncertainty Throughout the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/zpy25
te Brinke, L. W., van de Groep, S., van der Cruijsen, R., & Crone, E. (2021). Variability and change in adolescents’ prosocial behavior across multiple time scales. https://doi.org/10.31234/osf.io/cqntp
FAIR data and data sharing
We are working hard at making our research data Findable, Accessible, Interoperable and Reusable (FAIR) with help from our data manager. Some of our research data cannot be shared because of privacy considerations, but we are sharing data whenever possible.
Neurovault and publication packages:
We aim to upload all of our processed MRI data to NeuroVault and to share all materials and (anonymized) data belonging to a publication. If you are interested in data from one of our projects or articles, please don’t hesitate to contact us.
We collect a lot of MRI data, which is considered very sensitive data. Therefore, to determine what Dutch researchers are allowed to do with such data, our alumnus Dorien Huijser created a decision tree for researchers in the Netherlands
Aside from publication packages, which aim to increase reproducibility of results described in a manuscript, we have more initiatives related to reproducibility:
Eduard Klapwijk worked with Lara Wierenga and colleagues on the Qoala-T tool for quality control of FreeSurfer processed MRI data (https://doi.org/10.1016/j.neuroimage.2019.01.014, https://hdl.handle.net/10411/GXXAG3, https://github.com/Qoala-T/QC). Eduard also made a https://qoala-t.shinyapps.io/qoala-t_app/and a https://github.com/Qoala-T/QC/blob/master/Notebooks/Qoala-T_Notebook.ipynb, by which the Qoala-T model can be run with no coding required.
Eveline Crone joined a large collaboration to create a transparency checklist, which we use for our own outputs as well (Aczel et al., 2020, https://www.nature.com/articles/s41562-019-0772-6). A tool was also developed for this checklist, http://www.shinyapps.org/apps/ShortTransparencyChecklist/ .
Together, we are constantly exchanging skills. This helps us to share knowledge and collaborate:
To facilitate us learning from each other, we have set up a lab wiki. The lab wiki contains information on, for example, how to do data certain types of data analysis, data management, open science, literature search, but also reaching out to society. It is maintained by the entire SYNC lab via Github.
We have a Github which we use to collaborate on documents and code.
Open Science Community Rotterdam:
Many of us are members of the https://www.openscience-rotterdam.com/categories/people/, in which we can learn more about open science and get to know our fellow researchers at Erasmus University Rotterdam.