Papers



Submit a Paper!

Browse ReproHack papers

  • Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging

    Authors: Angela I. Renton, Thuy T. Dao, Tom Johnstone, Oren Civier, Ryan P. Sullivan, David J. White, Paris Lyons, Benjamin M. Slade, David F. Abbott, Toluwani J. Amos, Saskia Bollmann, Andy Botting, Megan E. J. Campbell, Jeryn Chang, Thomas G. Close, Monika Dörig, Korbinian Eckstein, Gary F. Egan, Stefanie Evas, Guillaume Flandin, Kelly G. Garner, Marta I. Garrido, Satrajit S. Ghosh, Martin Grignard, Yaroslav O. Halchenko, Anthony J. Hannan, Anibal S. Heinsfeld, Laurentius Huber, Matthew E. Hughes, Jakub R. Kaczmarzyk, Lars Kasper, Levin Kuhlmann, Kexin Lou, Yorguin-Jose Mantilla-Ramos, Jason B. Mattingley, Michael L. Meier, Jo Morris, Akshaiy Narayanan, Franco Pestilli, Aina Puce, Fernanda L. Ribeiro, Nigel C. Rogasch, Chris Rorden, Mark M. Schira, Thomas B. Shaw, Paul F. Sowman, Gershon Spitz, Ashley W. Stewart, Xincheng Ye, Judy D. Zhu, Aswin Narayanan & Steffen Bollmann
    DOI: https://doi.org/10.1038/s41592-023-02145-x
    Submitted by sbollmann    

    Why should we attempt to reproduce this paper?

    We invested a lot of work to make the analyses from the paper reproducible and we are very curious how the documentation could be improved and if people run into any problems.

  • PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

    Authors: Henry Charlesworth and Giovanni Montana
    Submitted by gmontana74      
      Mean reproducibility score:   10.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This paper proposes a probabilistic planner that can solve goal-conditional tasks such as complex continuous control problems. The approach reaches state-of-the-art performance when compared to current deep reinforcement learning algorithms. However, the method relies on an ensemble of deep generative models and is computationally intensive. It would be interesting to reproduce the results presented in this paper on their robotic manipulation and navigation problems as these are very challenging problems that current reinforcement learning methods cannot easily solve (and when they do, they require a significantly larger number of experiences). Can the results be reproduced out-of-the-box with the provided code?

  • REMoDNaV: robust eye-movement classification for dynamic stimulation

    Authors: Asim H. Dar, Adina S. Wagner, Michael Hanke
    DOI: https://doi.org/10.3758/s13428-020-01428-x
    Submitted by adswa    
      Mean reproducibility score:   7.0/10   |   Number of reviews:   3
    Why should we attempt to reproduce this paper?

    In theory, reproducing this paper should only require a clone of a public Git repository, and the execution of a Makefile (detailed in the README of the paper repository at https://github.com/psychoinformatics-de/paper-remodnav). We've set up our paper to be dynamically generated, retrieving and installing the relevant data and software automatically, and we've even created a tutorial about it, so that others can reuse the same setup for their work. Nevertheless, we've for example never tried it out across different operating systems - who knows whether it works on Windows? We'd love to share the tips and tricks we found to work, and even more love feedback on how to improve this further.

Search for papers

Filter by tags

Python R ArcGIS make Docker Drake Shiny LaTeX SVN knitr HPC Computer Science C Matlab Mathematica Stata Meta-analysis GDAL GEOS GIS PROJ Social Science swig miniconda neuroscience Jupyter Notebook tensorflow keras Pandas SQL Galaxies Astronomy Databases Binder tidyverse emacs literate earth sciences clumped isotopes org-mode geology eyetracking Git robotics deep learning planner reiforcement learning Plasma physics Hybrid-PIC EPOCH Laser Gamma-ray X-ray radiation Petawatt Fortran plasma PIC physics Monte Carlo Atomistic Simulation LAMMPS Electron Transport DFT descriptors interatomic potentials machine learning Molecular Dynamics Python scripting AIRSS structure prediction density functional theory high-throughput machine-learning RNA bioinformatics CFD Fluid Dynamics OpenFOAM C++ DNS Mathematics Droplets Basilisk Particle-In-Cell psychology consumer behavior number estimation mental arithmetic psychophysics Stan Finance SAS Replication crisis Economics Malaria Archaeology Precipitation Epidemiology Parkrun Health Health Economics HTA plumber science of science Zipf networks city size distribution urbanism literature review Preference Visual Questionnaire Mann-Whitney Correlation Conceptual replication Cognitive psychology Multinomial processing tree (MPT) modeling #urbanism #R k-means cluster analysis city-regions Urban Knowledge Systems Topic modelling Planning Support Systems Software Citation Quarto snakemake Numerical modelling Ocean climate physical oceanography apptainer oceanography R package structural equation modeling bayes factor Forest Simulations Models of forest dynamics multi-lab study mice mechanics growth Tissue Cells Clustering Expectation-Maximization bootstrapping R software Position Weight Matrices singularity neuroimaging effect size biology replicability cancer reproducibility csv osf preclinical research All tags Clear tags

Key

  Associated with an event
  Available for general review
  Public reviews welcome