TR&D Project 2: Data Model, Provenance and Integration Abstract Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Technology Research and Development Project, TR&D 2: Data Models and Integration, we will establish the technology for implementation of standardized workflow description and development of machine- readable markup and storage of the results of these workflows. The goals of this project are to encapsulate in a structured form the set of software tools utilized in a set of targeted workflows, to enable the markup of such analyses with important metadata, to determine the factors that contribute to the reproducibility of results or lack thereof, and to provide an application for scientists to interact with data models. To accomplish this, we will: 1) Use Linked Data Models to markup Data, Workflows, and Results; 2) Create reusable workflows with provenance tracking; and 3) Create BrainVerse: a suite of services for NIDM and computational reproducibility. We will work in partnership with the other TR&D projects, the Training and Dissemination Core and the Collaborator and Service Project users to deploy, test and validate the reproducible analysis system and to foster use of the reproducible framework in the neuroimaging research community.
| Solo, Victor; Poline, Jean-Baptiste; Lindquist, Martin A et al. (2018) Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE Trans Med Imaging 37:1537-1550 |
| Wimalaratne, Sarala M; Juty, Nick; Kunze, John et al. (2018) Uniform resolution of compact identifiers for biomedical data. Sci Data 5:180029 |
| Kennedy, David N (2018) Neuroimaging Neuroinformatics: Sample Size and Other Evolutionary Topics. Neuroinformatics 16:149-150 |
| Kim, Yang-Min; Poline, Jean-Baptiste; Dumas, Guillaume (2018) Experimenting with reproducibility: a case study of robustness in bioinformatics. Gigascience 7: |
| Guell, Xavier; Schmahmann, Jeremy D; Gabrieli, John DE et al. (2018) Functional gradients of the cerebellum. Elife 7: |
| Millman, K Jarrod; Brett, Matthew; Barnowski, Ross et al. (2018) Teaching Computational Reproducibility for Neuroimaging. Front Neurosci 12:727 |
| Nichols, Thomas E; Das, Samir; Eickhoff, Simon B et al. (2017) Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci 20:299-303 |
| Irimia, Andrei; Wei, Susan; Lu, Nanshu et al. (2017) Mobile Monitoring of Traumatic Brain Injury in Older Adults: Challenges and Opportunities. Neuroinformatics 15:227-230 |
| Ghosh, Satrajit S; Poline, Jean-Baptiste; Keator, David B et al. (2017) A very simple, re-executable neuroimaging publication. F1000Res 6:124 |
| Huntenburg, Julia M; Bazin, Pierre-Louis; Goulas, Alexandros et al. (2017) A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex. Cereb Cortex 27:981-997 |
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