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Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop (2020)

Chapter: Part 2: Different Types of Neuroscience Data: Challenges and Potential Opportunities

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Suggested Citation:"Part 2: Different Types of Neuroscience Data: Challenges and Potential Opportunities." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
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Suggested Citation:"Part 2: Different Types of Neuroscience Data: Challenges and Potential Opportunities." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
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Page 44

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Part 2 Different Types of Neuroscience Data: Challenges and Potential Opportunities Neuroscience data in the cloud spans multiple data types—clinical, genetic, neuroimaging, and real-world—as well as multiple modalities, spe- cies, and diseases, and thus requires robust and interoperable platforms, said Maryann Martone. Integrating across data types offers additional power and will also require novel analytical tools, added Silvana Borges. One plat- form that is working in this space is the BRAIN Commons,1 which according to Lee Lancashire, chief information officer at Cohen Veterans Bioscience, has developed a graph-based data model that allows users to capture multi­ modal data at the case level so that as new data are generated, they can be incorporated into the data model. Workshop participants also discussed features of other platforms that are integrating multiple sources and types of data, including the AMP-PD, Vivli (Chapter 7); the Psychiatric Genomics ­ Consortium (Chapter 8); the ­ ollaborative Informatics and Neuroimaging C ­ ­ Suite (COINS), CBRAIN, the Longitudinal Online Research and Imaging System (LORIS), the Canadian Open Science ­ latform (CONP), and the P National Data Archive (NDA) at NIMH (all in Chapter 9). In four breakout sessions, workshop participants discussed other chal- lenges and opportunities specific to different types of neuroscience data. These discussions are summarized in Chapters 7 through 10, which cover clinical trial and research data (Chapter 7); genetic data (Chapter 8); neuro­ imaging data (Chapter 9); and real-world data (Chapter 10). These dis- cussions were intended to be orthogonal to the discussions organized by 1  For more information, see https://www.braincommons.org/data (accessed November 11, 2019). 43 PREPUBLICATION COPY—Uncorrected Proofs

44 NEUROSCIENCE DATA IN THE CLOUD content area in Part 1. Workshop participants in these breakout sessions addressed many of the same issues as covered in Part 1, but through the lenses of different types of neuroscience data. PREPUBLICATION COPY—Uncorrected Proofs

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The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use.

To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop.

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