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  • Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

    Authors: Sahil Loomba, Alexandre de Figueiredo, Simon J. Piatek, Kristen de Graaf, Heidi J. Larson
    DOI: 10.1038/s41562-021-01056-1
    Submitted by samuelpawel      
      Mean reproducibility score:   8.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    In the middle of the COVID-19 pandemic, this paper provided important evidence regarding the effect of misinformation on vaccination intent. Its analyses and conclusions were extremely important for decision makers. Therefore, it is also important that the analyses are reproducible.

  • Machine learning a model for RNA structure prediction

    Authors: Nicola Calonaci, Alisha Jones, Francesca Cuturello, Michael Sattler, Giovanni Bussi
    DOI: 10.1093/nargab/lqaa090
    Submitted by giovannibussi      

    Why should we attempt to reproduce this paper?

    The method is trained on the data that were available, but it is meant to be re-trainable as soon as new data are published. It would be great to be really sure that even someone else will be able to do it. In case we receive any feedback, we would be really happy to improve our Github repository so as to make the reproduction easier!

  • pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

    Authors: Bonaretti S, Gold GE, Beaupre GS
    DOI: 10.1371/journal.pone.0226501
    Submitted by hub-admin    
      Mean reproducibility score:   6.5/10   |   Number of reviews:   2
    Why should we attempt to reproduce this paper?

    The paper describes pyKNEEr, a python package for open and reproducible research on femoral knee cartilage using Jupyter notebooks as a user interface. I created this paper with the specific intent to make both the workflows it describes and the paper itself open and reproducible, following guidelines from authorities in the field. Therefore, two things in the paper can be reproduced: 1) workflow results: Table 2 contains links to all the Jupyter notebooks used to calculate the results. Computations are long and might require a server, so if you want to run them locally, I recommend using only 2 or 3 images as inputs for the computations. Also, the paper should be sufficient, but if you need further introductory info, there are a documentation website: https://sbonaretti.github.io/pyKNEEr/ and a "how to" video: https://youtu.be/7WPf5KFtYi8 2) paper graphs: In the captions of figures 1, 4, and 5 you can find links to data repository, code (a Jupyter notebook), and the computational environment (binder) to fully reproduce the graph. These computations can be easily run locally and require a few seconds. All Jupyter notebooks automatically download data from Zenodo and provide dependencies, which should make reproducibility easier.

  • Dynamic redistribution of plasticity in a cerebellar spiking neural network reproducing an associative learning task perturbed by TMS

    Authors: Alberto Antonietti, Jessica Monaco, Egidio D'Angelo, Alessandra Pedrocchi, and Claudia Casellato
    Submitted by @_Aalph    

    Why should we attempt to reproduce this paper?

    Paper and codes+data have been published 4 years ago, will they still work? I always try to release data and codes to reproduce my papers, but I seldom receive feedback. It would be useful to have comments from a reproducers' team, in order to improve sharing for future research (I switched from MATLAB to Python already).