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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:   7.0/10   |   Number of reviews:   4
    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.

  • Droplet impact onto a spring-supported plate: analysis and simulations

    Authors: Michael J. Negus, Matthew R. Moore, James M. Oliver, Radu Cimpeanu
    DOI: https://doi.org/10.1007/s10665-021-10107-5
    Submitted by MNegus      
      Mean reproducibility score:   8.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    The direct numerical simulations (DNS) for this paper were conducted using Basilisk (http://basilisk.fr/). As Basilisk is a free software program written in C, it can be readily installed on any Linux machine, and it should be straightforward to then run the driver code to re-produce the DNS from this paper. Given this, the numerical solutions presented in this paper are a result of many high-fidelity simulations, which each took approximately 24 CPU hours running between 4 to 8 cores. Hence the difficulty in reproducing the results should mainly be in the amount of computational resources it would take, so HPC resources will be required. The DNS in this paper were used to validate the presented analytical solutions, as well as extend the results to a longer timescale. Reproducing these numerical results will build confidence in these results, ensuring that they are independent of the system architecture they were produced on.

  • The viewing angle in AGN SED models, a data-driven analysis

    Authors: Andrés Felipe Ramos Padilla, Lingyu Wang, Katarzyna Małek, Andreas Efstathiou, Guang Yang
    Submitted by aframosp    
      Mean reproducibility score:   9.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    Most of the material is available through Jupyter notebooks in GitHub, and it should be easy to reproduce with the help of Binder. With the notebooks, you could experiment with different parameters to the ones analyzed in the paper. It also contains a large dataset of physical parameters of galaxies analysed in this work. We expect this work to be easily reproducible in the steps described in the repository.

  • 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.

  • Hyperparameter importance Across Datasets

    Authors: Jan N van Rijn and Frank Hutter
    DOI: 10.1145/3219819.3220058
    Submitted by hub-admin    
      Mean reproducibility score:   7.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    I tried hard to make this paper as reproducible as possible, but as techniques and dependencies become more complex, it is hard to make it 100% clear. Any form of feedback is more than welcome.

  • Cell Contractility Facilitates Alignment of Cells and Tissues to Static Uniaxial Stretch

    Authors: Rens, E. G., & Merks, R. M. H.
    Submitted by hub-admin    
      Mean reproducibility score:   1.0/10   |   Number of reviews:   2
    Why should we attempt to reproduce this paper?

    This paper shows a fun and interesting simulation result. I find it (of course) very important that our results are reproducible. In this paper, however, we did not include the exact code for these specific simulations, but the results should be reproducible using the code of our previous paper in PLOS Computational Biology (Van Oers, Rens et al. https://doi.org/10.1371/journal.pcbi.1003774). I am genuinely curious to see if there is sufficient information for the Biophys J paper or if we should have done better. Other people have already successfully built upon the 2014 (PLOS) paper using our code; see e.g., https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.012408 and https://doi.org/10.1101/701037).

    Tags: C Matlab

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