Reproducibility and open science

I am the co-leader of the eScience Working Group on Reproducibility and Open Science. As part of the activities of the working group, we are interested in clarifying guidelines for steps that researchers at UW and elsewhere can take to increase the reproducibility and openess of their work. As an example, I am publishing here a few of the activities that I have undertaken to increase the reproducibility and openess of my own work

Where to find my published work:

Open access publications

Publications in open access journals

  1. R. Neto Henriques, A. Rokem, E. Garyfallidis, S. St-Jean, E.T. Peterson, M. Correia (2017). [Re] Optimization of a free water elimination two-compartment model for diffusion tensor imaging. ReScience `pdf`_
  2. A. Rokem, H. Takemura, A. Bock, K.S. Scherf, M. Behrmann, B.A. Wandell,
I. Fine, H. Bridge, F. Pestilli (2017). The visual white matter: The application of diffusion MRI and fiber tractography to vision science. Journal of Vision 17:4. pdf.
    1. Desimone, A. Rokem, K. Schneider (2016). Popeye: a population
receptive field esimation tool. Journal of Open Source Software: 10.21105/joss.00103. Paper. Software.
  1. S. Ajina, F. Pestilli, A. Rokem, C. Kennard, H. Bridge (2015). Human blindsight is mediated by an intact geniculo-extrastriate pathway. eLife 4:e08935, pdf.
  2. A. Rokem, J. Yeatman, F. Pestilli, K.N. Kay, A. Mezer, S. van der Walt, B.A. Wandell (2015). Evaluating the accuracy of diffusion MRI models in white matter. PLoS One, DOI: 10.1371/journal.pone.0123272. pdf. Code for this project is on Github
  3. E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. van der Walt, M. Descoteaux, I. Nimmo-Smith and Dipy Contributors (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics 8:8. link
  4. K.N. Kay, A. Rokem, J. Winawer, R.F. Dougherty B.A Wandell (2013). GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Frontiers in Brain Imaging Methods 7:247. link
  5. J.H. Yoon, S.L. Sheremata, A. Rokem, and M.A. Silver (2013). Windows to the soul: vision science as a tool for studying biological mechanisms of information processing deficits in schizophrenia. Frontiers in Psychopathology. 4:681 pdf
  6. K.N. Kay, J. Winawer, A. Rokem, A. Mezer and B.A. Wandell (2013) A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex. PLoS Computational Biology 9: e1003079. pdf
  7. A. Rokem and M.A. Silver (2013). The benefits of cholinergic enhancement during perceptual learning are long-lasting. Frontiers in Computational Neuroscience: 7:66 link
  8. A.A. Kosovicheva, S.L. Sheremata, A. Rokem, A.N. Landau, and M.A. Silver(2012). Cholinergic enhancement reduces orientation-specific surround suppression but not visual crowding. Frontiers in Behavioral Neuroscience 6: 61. pdf
  9. A. Rokem, J.H. Yoon, R.E. Ooms, R.J. Maddock, M. J. Minzenberg and M.A. Silver (2011). Broader visual orientation tuning in patients with schizophrenia. Frontiers in Human Neuroscience 5: 127 pdf
  10. H. Eyherabide, A. Rokem, A.V.M. Herz, I. Samengo (2009) Bursts generate a non-reducible spike-pattern code. Frontiers in Neuroscience 3: 8-14. pdf
  11. H.G. Eyherabide, A. Rokem, A.V.M. Herz and I. Samengo (2008). Burst Firing as a Neural Code in an Insect Auditory System. Frontiers in Computational Neuroscience 2:3. pdf

Slide decks from talks that I have given

Datasets that I have published:

Grasshopper data

I have made a couple of the data sets that I collected at the ITB available on the CRCNS website:


Nipy is a community which fosters the collaborative writing of reproducible research code for neuroimaging in the Python programming language. I have made contributiong to many of the libraries within this framework, and I am currently involved as a core developer in two libraries:

Other software projects:

For more code, take a look at my Github portfolio.