Neuroscience Data in the Cloud Opportunities and Challenges: Proceedings of a Workshop (2020) / Chapter Skim
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11 Future Directions
11 Future Directions
Pages 65-72
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From page 65... ...
. • Open-source data sharing has supported the development of new validated outcome measures and the identification of bio markers for multiple sclerosis; however, collecting data from multiple studies and companies presents legal and logistical challenges (Landsman, Weber)
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From page 66... ...
She said that when Cohen Veterans B iosciences faced a similar challenge in which they wanted to develop a platform that would facilitate big data modeling and cloud computing with multiple data types, they went through a 2-year exercise of evaluating hundreds of available platforms according to the 45 identified characteristics, winnowed down to the top 10 requirements (see Figure 11-1)
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The platform would support both data storage and cloud computing for a wide range of data modalities. NOTE: API = application program interface; FAIR = findable, accessible, interoperable, reusable.
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From page 68... ...
with SPINE (Structured Planning and Implementation of New Explorations) , a web-based, virtual laboratory designed to support the design and distributed execution of experiments centered on specific scientific questions.1 Using a federated database and cloud computing, this project aims to create local data repositories at multiple sites, 1 To learn more about SPINE, see https://cni.bwh.harvard.edu/front-and-back-end- development-spine-0 (accessed January 21, 2020)
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From page 69... ...
FUNDING: CURRENT COMMITMENTS AND FUTURE NEEDS Fundraising from governmental and private sources is needed to accomplish foundational work for data standardization, hosting, and reproducibility, and to build advanced cloud infrastructure, said Willke. Jonathan Cohen noted that the 5-year effort Willke described was funded entirely by Intel and is entirely open source, making it a model of academic–industrial collaboration.
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From page 70... ...
Some specific areas that could benefit from such proactive strategic planning that individual workshop participants raised included • Developing common data model frameworks and support for data transformations between platform or software versions, said Haas. Hill agreed that common data models are an important part of a potential solution that would address interoperability among platforms.
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From page 71... ...
Similarly, other areas that could benefit from collecting, evaluating, and comparing existing practices that individual participants discussed included reporting incidental findings in genetic analyses, data storage and prioritization, and sharing code with protections. Michael Milham noted that establishing best practices for assigning credit and incentivizing data sharing would be beneficial, particularly for tenure and promotion decisions.
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From page 72... ...
72 NEUROSCIENCE DATA IN THE CLOUD Although many challenges still need to be addressed, from technical to legal to ethical to cultural, there is also a great deal of excitement about what is possible, said Haas, and there is the will and ability to take on the challenges and usher in better -- and more widespread -- use of the cloud for neuroscience data.
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