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  • Christophe Hurlin

    () (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

  • Christophe Pérignon

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Victoria Stodden

    (Columbia University [New York])


We believe computational science as practiced today suffers from a growing credibility gap - it is impossible toreplicate most of the computational results presented at conferences or published in papers today. We argue that this crisis can be addressed by the open availability of the code and data that generated the results, in other words practicing reproducible computational science. In this paper we present a new computational infrastructure called that is designed to support published articles by providing a dissemination platform for the code and data that generated the their results. Published articles are given a companion webpage on the website from which a visitor can both download the associated code and data, and execute the code in the cloud directly through the website. This permits results to be verified through the companion webpage or on a user's local system. also permits a user to upload their own data to the companion webpage to check the code by running it on novel datasets. Through the creation of "coder pages" for each contributor to, we seek to facilitate social network-like interaction. Descriptive information appears on each coder page, including demographic data and other companion pages to which they made contributions. In this paper we motivate the rationale and functionality of and outline a vision of its future.

Suggested Citation

  • Christophe Hurlin & Christophe Pérignon & Victoria Stodden, 2012. " a novel dissemination and collaboration platform for executing published computational results," Working Papers halshs-00739233, HAL.
  • Handle: RePEc:hal:wpaper:halshs-00739233
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    References listed on IDEAS

    1. Bo Honore & Ekaterini Kyriazidou & J. L. Powell, 2000. "Estimation of tobit-type models with individual specific effects," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 341-366.
    2. Richard Duhautois, 2002. "Les réallocations d'emplois en France sont-elles en phase avec le cycle ?," Économie et Statistique, Programme National Persée, vol. 351(1), pages 87-103.
    3. G. S. Maddala, 1987. "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, University of Wisconsin Press, vol. 22(3), pages 307-338.
    4. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    5. C. A. Field & A. H. Welsh, 2007. "Bootstrapping clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 369-390.
    6. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
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    More about this item


    reproducible research; reproducible computational science; dissemination platform; collaborative networks; cloud computing; executable papers; code sharing; data sharing; open science;

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