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  • Planning Support Systems for Long-Term Climate Resilience: A Critical Review

    Authors: Supriya Krishnan, Nazli Yonca Aydin & Tina Comes
    DOI: https://doi.org/10.1007/978-3-030-76059-5_24
    Submitted by Supriya.kr09    
    Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This article used an open-source python repository for its analysis. It is well-suited for reproduction as more literature evolves on the intersection of urban planning and climate change. The adapted code is published alongside the article.

  • What do analyses of city size distributions have in common?

    Authors: Clémentine Cottineau
    DOI: 10.1007/s11192-021-04256-8
    Submitted by clementinecottineau      
      Mean reproducibility score:   8.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This article was meant to be entirely reproducible, with the data and code published alongside the article. It is however not embedded within a container (e.g. Docker). Will it past the reproducibility test tomorrow? next year? I'm curious.

  • Investigating the replicability of preclinical cancer biology

    Authors: Timothy M Errington, Maya Mathur, Courtney K Soderberg, Alexandria Denis, Nicole Perfito, Elizabeth Iorns, Brian A Nosek
    DOI: 10.7554/eLife.71601
    Submitted by samuelpawel      

    Why should we attempt to reproduce this paper?

    This papers represents an important milestone in meta-science, as it is one of the first large-scale replication projects outside the social sciences.

  • Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

    Authors: Bora Karasulu, Jean-Marc Leyssale, Patrick Rowe, Cedric Weber, Carla de Tomas
    DOI: 10.1016/j.carbon.2022.01.031
    Submitted by bkarasulu    
    Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This paper presents a fine example of high-throughput computational materials screening studies, mainly focusing on the carbon nanoclusters of different sizes. In the paper, a set of diverse empirical and machine-learned interatomic potentials, which are commonly used to simulate carbonaceous materials, is benchmarked against the higher-level density functional theory (DFT) data, using a range of diverse structural features as the comparison criteria. Trying to reproduce the data presented here (even if you only consider a subset of the interaction potentials) will help you devise an understanding as to how you could approach a high-throughput structure prediction problem. Even though we concentrate here on isolated/finite nanoclusters, AIRSS (and other similar approaches like USPEX, CALYPSO, GMIN, etc.,) can also be used to predict crystal structures of different class of materials with applications in energy storage, catalysis, hydrogen storage, and so on.

  • New Insight into the Stability of CaCO3 Surfaces and Nanoparticles via Molecular Simulation

    Authors: A. Matthew Bano, P. Mark Rodger, and David Quigley
    DOI: 10.1021/la501409j
    Submitted by dquigley      

    Why should we attempt to reproduce this paper?

    The negative surface enthalpies in figure 5 are surprising. At least one group has attempted to reproduce these using a different code and obtained positive enthalpies. This was attributed to the inability of that code to independently relax the three simulation cell vectors resulting in an unphysical water density. This demonstrates how sensitive these results can be to the particular implementation of simulation algorithms in different codes. Similarly the force field used is now very popular. Its functional form and full set of parameters can be found in the literature. However differences in how different simulation codes implement truncation, electrostatics etc can lead to significant difference in results such as these. It would be a valuable exercise to establish if exactly the same force field as that used here can be reproduced from only its specification in the literature. The interfacial energies of interest should be reproducible with simulations on modest numbers of processors (a few dozen) with run times for each being 1-2 days. Each surface is an independent calculation and so these can be run concurrently during the ReproHack.

  • 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?

  • 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.

  • Explicit (but not implicit) environmentalist identity predicts pro-environmental behavior and policy preferences

    Authors: Brick, C., & Lai, C. K.
    DOI: 10.1016/j.jenvp.2018.07.003
    Submitted by hub-admin    
      Mean reproducibility score:   6.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    The results of the individual studies (4) could be interpreted in support for the hypothesis, but the meta-analysis suggested that implicit identification was not a useful predictor overall. This conclusion is an important goalpost for future work.

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