Diversity in Reproducibility

Olivia Guest 1 Nicolas Rougier 2
2 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : In our previous contribution, we proposed computational modelling-related definitions for replicable, i.e., experiments within a model can be recreated using its original codebase, and reproducible, i.e., a model can be recreated based on its specification. We stressed the importance of specifications and of access to codebases. Furthermore, we highlighted an issue in scholarly communication — many journals do not require nor facilitate the sharing of code. In contrast, many third-party services have filled the gaps left by traditional publishers (e.g., Binder, 2016; GitHub, 2007; Open Science Framework, 2011; ReScience, 2015). Notwithstanding, journals and peers rarely request or expect use of such services. We ended by asking: are we ready to associate codebases with articles and are we prepared to ensure computational theories are well-specified and coherently implemented?
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Contributeur : Nicolas P. Rougier <>
Soumis le : lundi 24 avril 2017 - 20:12:09
Dernière modification le : jeudi 11 janvier 2018 - 06:24:26
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  • HAL Id : hal-01513273, version 1


Olivia Guest, Nicolas Rougier. Diversity in Reproducibility. IEEE CDS Newsletter, IEEE CIS, 2016, 13 (2), 〈https://openlab-flowers.inria.fr/t/ieee-cis-newsletter-on-cognitive-and-developmental-systems/129〉. 〈hal-01513273〉



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