recommended readings

Version control

Ram, K (2013). Git can facilitate greater reproducibility and increased transparency in science. Source Code for Biology and Medicine. DOI: 10.1186/1751-0473-8-7

        Steps to get started (recommendations from Bram):

  1. Install git on your computer (If you are working on the computer cluster at the DCCN, then you should be good to go). You can follow these instructions by Software Carpentry. Also install

  2. Set up an account at GitHub.

  3. Take the self-paced tutorial "Version Control with Git" from Software Carpentry on your own. This takes about 3 hours in total, which you can spread out as you like.

  4. You can also have a look at Danae's GitHub tutorial on how to get started.

        After having done the tutorial, you might not like doing version control via the command line. You don't have to:

  • There are many graphical user interface clients for Git. For an overview, see here.

  • Many software packages we use for coding and data analysis come with Git and GitHub integration. This means that you can run all version control commands via the graphical user interface of these programs. Among others, the following programs include these features: RStudio (for R, see here), PyCharm (for Python, see here), and MATLAB (see here).

        If you get stuck, you will probably find the solution to your problem on Stack Overflow.

Research documentation inspiration

Research compendium for the report on independent race model analysis of selective stopping by Zandbelt & Van den Bosch

Research compendium for the report on the cognitive mechanisms of the defer-speedup and date-delay framing effects in intertemporal choice by Zandbelt

Research compendium for the report on the cognitive and neural mechanisms of selective stopping by Zandbelt & Van den Bosch

Data quality control

Qualitative Quality Control Manual: Artifacts in structural and functional MRI.

fMRI design

Henson, R. (2007). Efficient Experimental Design for fMRI. 

Liu, T.T., et al. (2001). Detection Power, Estimation Efficiency, and Predictability in Event-Related fMRI. NeuroImage 13, 759 –773.

Mumford, J.A., Poline, J-B., Poldrack, R.A. (2015). Orthogonalization of Regressors in fMRI Models. PLoS ONE 10(4): e0126255.

Wager, T.D., & Nichols, T.E. (2003). Optimization of experimental design in fMRI: a general framework using a genetic algorithm. NeuroImage 18 (2003) 293–309.


Data visualization

Allen, E. A., Erhardt, E. B., & Calhoun, V. D. (2012). Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality. Neuron, 74(4), 603–608.

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554.

Cleveland, W. S., & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, 229(4716), 828–833.

Heer, J., & Bostock, M. (2010). Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 203–212). New York, NY, USA: ACM.

Wainer, H. (1984). How to display data badly. The American Statistician, 38(2), 137–147.

Science communication

Nichols, T.E et al. (2016) Best Practices in Data Analysis and Sharing in Neuroimaging using MRI. CORBIDASreport. See section 6 on reporting results.

Poldrack, R.A. (2008). Guidelines for reporting an fMRI study. Neuroimage. 40(2): 409–414. doi:  10.1016/j.neuroimage.2007.11.048


Research data management

Donders research data management (DRDR).