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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, 16 Olivia Guest 17 Matt G. Hall 18 Rafael Neto Henriques 18, 19 Xavier Hinaut 20, 1 Kamil S Jaron 21 Mehdi Khamassi 15 Almar Klein 22 Tiina Manninen 23 Pietro Marchesi 24 Dan Mcglinn 25 Christoph Metzner 26 Owen L. Petchey 27 Hans Ekkehard Plesser 28 Timothée Poisot 29 Karthik Ram 30 Yoav Ram 31 Etienne Roesch 32, 33 Cyrille Rossant 18 Vahid Rostami 28 Aaron Shifman 34 Joseph Stachelek 35 Marcel Stimberg 36, 37 Frank Stollmeier 38 Federico Vaggi 39 Guillaume Viejo 15 Julien Vitay 40 Anya Vostinar 41 Roman Yurchak 42 Tiziano Zito 43
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
16 AMAC
ISIR - Institut des Systèmes Intelligents et de Robotique
20 KT - Knowledge Technology group [Hamburg]
Department of Informatics [Hamburg]
39 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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https://hal.inria.fr/hal-01592078
Contributor : Nicolas P. Rougier <>
Submitted on : Friday, September 22, 2017 - 3:28:31 PM
Last modification on : Tuesday, September 22, 2020 - 3:47:19 AM

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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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