The Importance of Standards for Sharing of Computational Models and Data


The target article by Lee et al. (in review) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.

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Development of this article was supported by the BRAIN Initiative and National Institute of Mental Health (1R24MH114705). P.R. funding sources: H2020 VirtualBrainCloud 826421, Human Brain Project 785907 and ERC 683049; German Research Foundation CRC 1315, CRC 936 and RI 2073/6-1; Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative.

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Correspondence to Russell A Poldrack.

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Poldrack, R.A., Feingold, F., Frank, M.J. et al. The Importance of Standards for Sharing of Computational Models and Data. Comput Brain Behav 2, 229–232 (2019).

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  • Data sharing
  • Standards
  • Reproducibility
  • Computational models