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.

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. For example, 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.

Neurovault CollectionsPublication PackagesDecision tree MRI data sharing
Achterberg et al., (2020).
https://identifiers.org/neurovault.collection:6070

Van der Cruijsen et al., (2019).
https://identifiers.org/neurovault.collection:4774

Van de Groep et al., (2020).
https://identifiers.org/neurovault.collection:6546 
Publication packages (manuscript, data, and code belonging to a publication, aimed at increasing reproducibility of the results found):

Achterberg et al. (2020). Longitudinal changes in DLPFC activation during childhood are related to decreased aggression following social rejection, https://hdl.handle.net/10411/BE2X5L, DataverseNL, V1.

Klapwijk et al. (2019). Qoala-T: A supervised-learning tool for quality control of FreeSurfer, https://hdl.handle.net/10411/GXXAG3, DataverseNL, V1.

Van de Groep, S. et al. (2019). Giving to friends, classmates, and strangers in adolescence, https://hdl.handle.net/10411/M7YIBE, DataverseNL, V1

Van der Cruijsen et al. (2019). Alexithymic traits can explain the association between puberty and symptoms of depression and anxiety in adolescent females, https://hdl.handle.net/10411/RNLIVC, DataverseNL, V1

Van de Groep, S. et al. (2019). Developmental Changes and Individual Differences in Trust and Reciprocity in Adolescence, https://hdl.handle.net/10411/9LHV2C, DataverseNL, V1
We collect a lot of MRI data, which is considered very sensitive data. Therefore, in order to determine what Dutch researchers are allowed to do with such data, our data manager Dorien Huijser created a decision tree for researchers in the Netherlands: see this link.

Open access
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. You can find some examples below:

Gold Open AccessGreen Open AccessPreprints
Achterberg, M., & van der Meulen, M. (2019). Genetic and environmental influences on MRI scan quantity and quality. Developmental Cognitive Neuroscience, 38, 100667. https://doi.org/10.1016/j.dcn.2019.100667
Van der Cruijsen, R., Buisman, R., Green, K., Peters, S., & Crone, E. (2019). Neural responses for evaluating self and mother traits in adolescence depend on mother–adolescent relationships. Social Cognitive and Affective Neuroscience, 14(5), 481-492. https://doi.org/10.1093/scan/nsz023. Repository link: http://hdl.handle.net/1887/85298
Klapwijk, E., van den Bos, W., Tamnes, C. K., Mills, K. L., & Raschle, N.(2019, December 18). Opportunities for increased reproducibility and replicability of developmental cognitive neuroscience. PsyArXiv. https://doi.org/10.31234/osf.io/fxjzt

Reproducibility
Aside from publication packages, which aim to increase reproducibility of results described in a manuscript, we have more initiatives related to reproducibility:

Quality ControlTransparancy checklistfMRI analysis pipelineReproducibility in Developmental NeuroimagingParadigm validationLongitudinal Reliability
Eduard Klapwijk worked with Lara Wierenga and colleagues on the Qoala-T tool for quality control of FreeSurfer processed MRI data (open access manuscript, publication package, Github). Eduard also made a Qoala-T shinyapp and a Jupyter notebook, 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, open access manuscript). A tool was also developed for this checklist, both for the complete checklist as well as a shortened version.
Eduard Klapwijk, Philip Brandner and Suzanne van de Groep are currently developing a standardized pipeline within the Nipype framework to analyze fMRI data. These scripts will be made publicly available as soon as possible, and will provide a reproducible and readable version of our fMRI analyses.
Eduard Klapwijk wrote an article with a group of international collaborators about increasing reproducibility in developmental neuroimaging. 
In order to thoroughly test new paradigms, Michelle Achterberg and Mara van der Meulen used a pilot, test and replication design within the same project and then combined results meta-analytically (see Achterberg et al. (2017) and Van der Meulen et al. (2017)).
We work a lot with longitudinal data, which makes it very important to examine the reliability of our measures. One example can be found in Peters et al. (2016).

Collaboration 
We feel that close collaboration, both with each other and others, is paramount for conducting high-quality research. In order to collaborate, we use multiple tools and communities:

Lab wikiSURF Research DriveLab Github PageLab OSF PageOpen Science Community RotterdamMulti-Site Projects
Together, we are constantly exchanging skills. 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 use the SURF Research Drive from Erasmus University Rotterdam, a platform comparable to a Google Drive for research groups, which allows us to access our data from anywhere and safely share (parts of) our data with collaborators during our research.
We have a lab github page which we use to collaborate on documents and code.
We have a lab OSF page which we use to store project documents on.
Many of us are members of the Open Science Community Rotterdam, in which we can learn more about open science and get to know our fellow researchers at Erasmus University Rotterdam.
With colleagues from four different countries, we are collaborating on a multi-site project to examine structural brain development in childhood and adolescence. We contributed longitudinal structural MRI data from the BrainTime project, conducted at Leiden University. This resulted in two articles: Mills et al. (2016) and Tamnes et al (2017). We are currently also working on a continuation of this project with DTI data.