Abstract
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.
Highlights
The challenge of reproducibility in science (Campbell, 2016) has compelled the neuroscience research community to adopt new approaches to ensure scientific reliability without impeding innovation
In tandem with the deployment of a robust cyberinfrastructure, key enhancements to organizational practices are necessary for Open Science to truly proliferate
There are some risks and challenges associated in the adoption of an Open Science framework
Summary
The challenge of reproducibility in science (Campbell, 2016) has compelled the neuroscience research community to adopt new approaches to ensure scientific reliability without impeding innovation. Institutional sharing aims to prevent data loss, increase sample size and statistical power, and reduce acquisition costs by encouraging data re-use (thereby maximizing returns on public funding) In addition to these advantages, inviting external researchers to access these institutional resources will expand the reach and impact of research conducted at the institute (Poldrack and Gorgolewski, 2014). Governments and funding agencies in the USA (National Institutes of Health, 2014; National Institute of Mental Health, 2015), Canada (Tri-Agency Statement of Principles of Digital Data Management, 2016), Europe (Horizon 2020, The Wellcome Trust, 2016) and elsewhere encourage and increasingly require research programs to establish data management and sharing plans from the start of the research data lifecycle Despite these efforts, such initiatives are frequently constrained to particular projects or focused collaborations rather than institutional initiatives, as the sharing of data often remains at the discretion of individual investigators whose technical resources and expertise in data infrastructure may be limited
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