Sustainable computational science: the ReScience initiative

Research output: CommunicationJournal article

  • Nicolas Rougier
  • Konrad Hinsen
  • Frédéric Alexandre
  • Thomas Arildsen
  • Lorena Barba
  • Fabien Benureau
  • C. Titus Brown
  • Pierre de Buyl
  • Ozan Caglayan
  • Andrew Davison
  • Marc André Delsuc
  • Georgios Detorakis
  • Alexandra Diem
  • Damien Drix
  • Pierre Enel
  • Benoît Girard
  • Olivia Guest
  • Matt Hall
  • Rafael Henriques
  • Xavier Hinaut
  • Kamil Jaron
  • Mehdi Khamassi
  • Almar Klein
  • Tiina Manninen
  • Pietro Marchesi
  • Dan McGlinn
  • Christoph Metzner
  • Owen Petchey
  • Hans Ekkehard Plesser
  • Timothée Poisot
  • Karthik Ram
  • Yoav Ram
  • Etienne Roesch
  • Cyrille Rossant
  • Vahid Rostami
  • Aaron Shifman
  • Joseph Stachelek
  • Marcel Stimberg
  • Frank Stollmeyer
  • Federico Vaggi
  • Guillaume Viejo
  • Julien Vitay
  • Anya Vostinar
  • Roman Yurchak
  • Tiziano Zito

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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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.
Original languageEnglish
JournalarXiv.org (e-prints)
Number of pages8
StateIn preparation - 14 Jul 2017
Publication categoryCommunication

    Research areas

  • Open Science, computational science, Reproducibility, replicability, Journal, Publication
ID: 260983949