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Sustainable computational science: the ReScience initiative

Nicolas Rougier 1 Konrad Hinsen 2 Frédéric Alexandre 1 Thomas Arildsen 3 Lorena Barba 4 Fabien C. Y. Benureau 1 C. Titus Brown 5 Pierre de Buyl 6 Ozan Caglayan 7 Andrew P. Davison 8 Marc André Delsuc 9 Georgios Detorakis 10 Alexandra K. Diem 11 Damien Drix 12 Pierre Enel 13 Benoît Girard 14, 15 Olivia Guest 16 Matt G. Hall 17 Rafael Neto Henriques 17, 18 Xavier Hinaut 19, 1 Kamil S Jaron 20 Mehdi Khamassi 15 Almar Klein 21 Tiina Manninen 22 Pietro Marchesi 23 Dan Mcglinn 24 Christoph Metzner 25 Owen L. Petchey 26 Hans Ekkehard Plesser 27 Timothée Poisot 28 Karthik Ram 29 Yoav Ram 30 Etienne Roesch 31, 32 Cyrille Rossant 17 Vahid Rostami 27 Aaron Shifman 33 Joseph Stachelek 34 Marcel Stimberg 35, 36 Frank Stollmeier 37 Federico Vaggi 38 Guillaume Viejo 15 Julien Vitay 39 Anya Vostinar 40 Roman Yurchak 41 Tiziano Zito 42
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
19 KT - Knowledge Technology group [Hamburg]
Department of Informatics [Hamburg]
38 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.
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Contributor : Nicolas P. Rougier <>
Submitted on : Friday, September 22, 2017 - 3:28:31 PM
Last modification on : Thursday, March 5, 2020 - 6:35:33 PM

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Nicolas Rougier, Konrad Hinsen, Frédéric Alexandre, Thomas Arildsen, Lorena Barba, et al.. Sustainable computational science: the ReScience initiative. PeerJ Computer Science, PeerJ, 2017, 3, pp.e142. ⟨10.7717/peerj-cs.142⟩. ⟨hal-01592078⟩



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