2019
DOI: 10.1007/s42113-019-00062-x
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The Importance of Standards for Sharing of Computational Models and Data

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 … Show more

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Cited by 13 publications
(8 citation statements)
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“…Thus, while it may superficially appear that we are at odds with the emphasis on the bottom few steps in our path model (hypothesis testing and data analysis, recall Figure 2) by those who are investigating replicability, we are comfortable with this emphasis. We believe the proposals set out by some to automate or streamline the last few steps are part of the solution (e.g., Lakens & DeBruine, 2020;Poldrack et al, 2019). Such a division of labor, might help maximise the quality of theories and showcase the contrast -which Meehl (1967) and others have drawn attention to -between substantive theories and the hypotheses they generate.…”
Section: A Way Forwardmentioning
confidence: 99%
“…Thus, while it may superficially appear that we are at odds with the emphasis on the bottom few steps in our path model (hypothesis testing and data analysis, recall Figure 2) by those who are investigating replicability, we are comfortable with this emphasis. We believe the proposals set out by some to automate or streamline the last few steps are part of the solution (e.g., Lakens & DeBruine, 2020;Poldrack et al, 2019). Such a division of labor, might help maximise the quality of theories and showcase the contrast -which Meehl (1967) and others have drawn attention to -between substantive theories and the hypotheses they generate.…”
Section: A Way Forwardmentioning
confidence: 99%
“…The micro-and meso-scale model consists of neuronal mass models or assembles of neuronal mass models, generating the so-called mass field. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including EEG and MEG [426][427][428][429]. The Virtual Brain has been used for many applications to investigate different diseases, such as neurodegenerative disorders [430], PD [431], dementia [432], TBI [433], AD [434][435][436], epilepsy [437][438][439][440], brain tumor [441], chronic stroke [442], or physiological brain dynamics [431,443,444].…”
Section: Multi-scale Models Represent the Ultimate Translational Appr...mentioning
confidence: 99%
“…In neuroscience specifically, platforms such as OpenNeuro (https://openneuro.org/) have been developed to openly share neuroimaging datasets, and the 'Brain Imaging Data Structure' (BIDS; https://bids.neuroimaging.io/; has been developed to homogenize the structure of datasets, which fosters time-efficient use of datasets and development of code tailored to this format (see, e.g., fMRIPrep, Esteban et al, 2019;and MRIQC, Esteban et al, 2017), allowing the same code to be easily applied to multiple datasets. Note that similar homogeneous standards for the structure of computational models have also been proposed (Poldrack et al, 2019). Platforms such as the "NeuroImaging Tools & Resources Collaboratory" (NITRC; https://www.nitrc.org/) are used for data as well as software sharing.…”
Section: Improvement Referencementioning
confidence: 99%