The Importance of Standards for Sharing of Computational Models and Data
Year: 2019
Type: article
Abstract: The Target Article by Lee et al. (2019) 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.
Source: Computational Brain & Behavior
Institutions: Stanford University, Brown University, University College London, University of Zurich, Vrije Universiteit Amsterdam, King's College London, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charité – Universitätsmedizin Berlin, University of Wisconsin–Madison, The Ohio State University, The University of Texas at Austin, National Institutes of Health, National Institute of Mental Health, Princeton University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: NeuroScout: A cloud-based platform for flexible re-analysis of naturalistic fMRI datasets, BIDS-Derivatives: A data standard for derived data and models in the BRAIN Initiative, BRAIN INITIATIVE RESOURCE: DEVELOPMENT OF A HUMAN NEUROELECTROMAGNETIC DATA ARCHIVE AND TOOLS RESOURCE (NEMAR), EEGLab: Software Analysis of Human Brain Dynamics
OpenAlex ID: W2979976624