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PI: Pestilli, Garyfallidis, Henschel, Wang, Dinov
Summary:
The project focuses on providing seamless public access to data, computing, and reproducible algorithms, while promoting code sharing and upcycling the long tail of neuroscience data.
External Link: NCS-FO: Connectome mapping algorithms with application to community services for big data neuroscience
Funding:
Details:
Neuroscience is engaging at the forefront of science by dissolving disciplinary boundaries and promoting transdisciplinary research. This is a process that, in principle, can facilitate discovery by convergent efforts from theoretical, experimental and cognitive neuroscience, as well as computer science and engineering. To assure the success of this process the current lack of established mechanisms to guarantee reproducibility of scientific results must be overcome. Promoting open software and data sharing has become paramount to address reproducibility. This project addresses challenges to neuroscience reproducibility by providing integrative mechanisms for publishing data, and algorithms while embedding them with compute resources to impact multiple scientific communities.
We propose a grassroots approach to open neuroscience, based on give-to-get mechanisms with the goal to gather the long tail of brain data collected nationwide. We will develop a platform to publish reproducible code with seamless access to national supercomputers and reference datasets for data quality and results validation. We will promote voluntary data submissions in exchange for reproducible algorithms and high-throughput computing to contribute upcycling the long tail of neuroimaging data. We will develop technology for open online research tools and make them freely available in exchange for data to pursuit gathering diverse neuroimaging data sets and redistribute them across research communities in multiple domains of psychology, engineering, computer science and neuroscience. The products of the project will combine modern web, visualization, and database technologies as well as network neuroscience and image processing algorithms into a unique online platform. The platform will be available at brain-life.org to expand the userbase of brain data and advance workforce training.