Das Samir, Glatard Tristan, Rogers Christine, Saigle John, Paiva Santiago, MacIntyre Leigh, Safi-Harab Mouna, Rousseau Marc-Etienne, Stirling Jordan, Khalili-Mahani Najmeh, MacFarlane David, Kostopoulos Penelope, Rioux Pierre, Madjar Cecile, Lecours-Boucher Xavier, Vanamala Sandeep, Adalat Reza, Mohaddes Zia, Fonov Vladimir S, Milot Sylvain, Leppert Ilana, Degroot Clotilde, Durcan Thomas M, Campbell Tara, Moreau Jeremy, Dagher Alain, Collins D Louis, Karamchandani Jason, Bar-Or Amit, Fon Edward A, Hoge Rick, Baillet Sylvain, Rouleau Guy, Evans Alan C
McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada; Montreal Neurological InstituteMontreal, QC, Canada.
Department of Computer Science and Software Engineering, Concordia University Montreal, QC, Canada.
Front Neuroinform. 2017 Jan 6;10:53. doi: 10.3389/fninf.2016.00053. eCollection 2016.
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.
随着技术的成熟以及全球研究与交流的多样化，数据共享正日益成为一项要求。因此，研究人员正在寻求切实可行的解决方案，不仅是为了加强科学合作，也是为了获取更多数据以及访问专门的数据集。在许多情况下，数据采集的实际情况带来了巨大负担，因此访问公共数据集有助于进行更强大的分析和更广泛丰富的数据探索。为了满足这一需求，蒙特利尔神经病学研究所宣布致力于开放科学，利用将临床和研究数据提供给全世界的力量（欧文斯，2016a，b）。因此，LORIS和CBRAIN（达斯等人，2016）平台承担了机构层面实施开放数据共享所特有的技术挑战，包括：多模态数据（表型、临床、神经影像、生物样本库和基因组学等）的全面链接；专门为机构和多项目数据共享设计的安全数据库加密，确保受试者保密（使用多层标识符）；具有单研究和机构多级权限的查询功能，允许共享所有已同意且已去识别的受试者数据的公共数据；可配置的管道和标记，以促进采集和分析，以及访问高性能计算集群以进行快速数据处理和软件工具共享；强大的工作流程和质量控制机制，确保最佳实践的透明度和一致性；数据的长期存储（和网络访问），减少机构数据资产的损失；增强基于网络的成像、基因组和表型数据可视化，允许从世界任何地方实时查看和操作数据；用于数据过滤、汇总统计以及个性化和可配置仪表板的众多模块。在蒙特利尔神经病学研究所实现开放科学的愿景将是一项协同努力，旨在为全球研究界促进数据共享。我们的目标是利用在多站点协作研究基础设施方面的多年经验，以切实可行且稳健的方式实施技术要求，以实现这种程度的公共数据共享，支持加速科学发现。
