Frontiers in Neuroinformatics · 2017 · 38 citations · 41 references
ComputingBiomedical InformaticsNeuroscienceData ManagementDatabasesHealth Data InteroperabilityData ConfidentialityData InteroperabilityData SecurityData SharingOpen DataParticipant ConfidentialityClinical Data SharingConfidentialityCross-institution Data SharingData Access For ResearchersData ExplorationDatabase EncryptionMultimodal Data AcquisitionResearch Data Infrastructure
Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b). As such, the LORIS and CBRAIN (Das et al., 2016) platforms have been tasked with the technical challenges specific to the institutional-level implementation of open data sharing, including: Comprehensive linking of multimodal data (phenotypic, clinical, neuroimaging, biobanking, and genomics, etc.)Secure database encryption, specifically designed for institutional and multi-project data sharing, ensuring subject confidentiality (using multi-tiered identifiers).Querying capabilities with multiple levels of single study and institutional permissions, allowing public data sharing for all consented and de-identified subject data.Configurable pipelines and flags to facilitate acquisition and analysis, as well as access to High Performance Computing clusters for rapid data processing and sharing of software tools.Robust Workflows and Quality Control mechanisms ensuring transparency and consistency in best practices.Long term storage (and web access) of data, reducing loss of institutional data assets.Enhanced web-based visualization of imaging, genomic, and phenotypic data, allowing for real-time viewing and manipulation of data from anywhere in the world.Numerous modules for data filtering, summary statistics, and personalized and configurable dashboards. Implementing the vision of Open Science at the Montreal Neurological Institute will be a concerted undertaking that seeks to facilitate data sharing for the global research community. Our goal is to utilize the years of experience in multi-site collaborative research infrastructure to implement the technical requirements to achieve this level of public data sharing in a practical yet robust manner, in support of accelerating scientific discovery.
41
Paul A. Harris, Robert Taylor, Robert J. Thielke et al. · Journal of Biomedical Informatics · 2008 · 51.7K citations · Full text
The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg et al. · Scientific Data · 2016 · 17.8K citations · Full text
Statistical parametric maps in functional imaging: A general linear approach
Karl Friston, Andrew P. Holmes, Keith J. Worsley et al. · Human Brain Mapping · 1994 · 9.8K citations
Toward discovery science of human brain function
Bharat B. Biswal, Maarten Mennes, Xi‐Nian Zuo et al. · Proceedings of the National Academy of Sciences · 2010 · 3.1K citations · Full text