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REMoDNaV: Robust Eye Movement Detection for Natural Viewing

This repository contains the raw data, the code to generate summary statistics, and raw figures for the manuscript, and the manuscript sources for the publication REMoDNaV: Robust Eye Movement Detection for Natural Viewing.

Updated instructions for computing the results and building the manuscript

[See below for the original instructions]

More than three years after the journal publication, the original setup for reproducing the statistical results and figures in the manuscript started to fail. Software environments had advanced sufficiently to make changes to the analysis code necessary. The need to pin the packages used for figure generation to retain identical outputs further complicated the recreation of a functional computation environment. To compensate and add longevity, the computational pipeline was moved to a Docker-based environment.

A protocol of this change is available at #24

The one (minor) difference in the results, compared to the original publication, is detailed at #20 (comment)

In order to reproduce the results and the manuscript, the following software needs to be installed:

Installation instructions are provided on the respective websites. For DataLad we recommend the instructions in its handbook at

This following procedure has been verified to work on Debian, MacOS, and Windows 10.

Obtain this repository with DataLad. It is fully self-contained, and includes versioned links to all code, data and computational environments:

C:\Users\mih>datalad clone

To verify the computational reproducibility of any numerical value reported in the paper, and the SVG code for all figures, enter the dataset and run:

C:\Users\mih>cd paper-remodnav
C:\Users\mih\paper-remodnav>datalad rerun results-containerized

This will recompute everything. In order to do this, a total of ~1GB of input data (detailed in the manuscript) will be downloaded. In addition, about 1.8GB for the Docker container image are downloaded. Apart from the time needed to download all information, the actual computation only takes a few minutes.

If recomputation is successful and reproducible, no change to the dataset will be saved (indicated by save (notneeded)). Any bit-precision difference will otherwise be detected and can be inspected in the form of the change record (last commit). The to-be-reproduced state is captured by the signed tag results-containerized.

The full manuscript can be built with the command:

C:\Users\mih\paper-remodnav>datalad containers-run -n docker-make main.pdf

Old instructions for computing the results and building the manuscript

To recompute results and compile the paper, do the following:

    # one way to create a virtual environment:
    virtualenv --python=python3 ~/env/remodnav
    . ~/env/remodnav/bin/activate
  • clone the repository with git clone
  • Navigate into the repository and run make to compile the paper as it was published.
  • To recompute results and figures, run make clean, followed by make.

Appropriate Makefiles within the directory will install necessary Python requirements (the remodnav Python package, datalad, pandas, seaborn, and sklearn), execute data retrieval via DataLad (about 550MB in total), compute the results and figures from code/, insert the results and rendered figures in the main.tex file, and render the PDF. The full PDF will be main.pdf.

Software requirements

Note that inkscape, latexmk, and texlive-latex-extra need to be installed on your system to render the figures and the PDF.

Getting help

If you encounter failures, e.g. due to uninstalled python modules, restart make after running make clean. If you encounter failures you suspect are due to deficiencies in this repository, please submit an issue or a pull request. Please address issues on bugs or questions of other software to the software's specific home repository.