NDI: A Platform-Independent Data Interface and Database for Neuroscience Physiology and Imaging Experiments
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2022-01
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<jats:title>Abstract</jats:title> <jats:p>Collaboration in neuroscience is impeded by the difficulty of sharing primary data, results, and software across labs. Here, we introduce Neuroscience Data Interface (NDI), a platform-independent standard that allows an analyst to use and create software that functions independently from the format of the raw data or the manner in which the data are organized into files. The interface is rooted in a simple vocabulary that describes common apparatus and storage devices used in neuroscience experiments. Results of analyses, and analyses of analyses, are stored as documents in a scalable, queryable database that stores the relationships and history among the experiment elements and documents. The interface allows the development of an application ecosystem where applications can focus on calculation rather than data format or organization. This tool can be used by individual labs to exchange and analyze data, and it can serve to curate neuroscience data for searchable archives.</jats:p>
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García Murillo, Daniel, Yixin Zhao, Ora S Rogovin, Kelly Zhang, Andrew W Hu, Mo Re Kim, Shufei Chen, Ziqi Wang, et al. (2022). NDI: A Platform-Independent Data Interface and Database for Neuroscience Physiology and Imaging Experiments. eneuro, 9(1). pp. ENEURO.0073–21.2022. 10.1523/eneuro.0073-21.2022 Retrieved from https://hdl.handle.net/10161/34310.
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Yannan Zhu
Yannan is a Ph.D. student in the Cognitive Neuroscience Admitting Program. She graduated from Brandeis University in 2023 with a B.S. in Neuroscience and Psychology, and then worked for two years as the Lab Manager/Research Assistant with Dr. Nick Turk-Browne at Yale University. Yannan’s research explores the intersection between learning, memory, and neuromodulation, including how we extract schematic information from memory episodes, how the brain state evolves as we learn, and how endogenous neuromodulation would improve learning and adaptive behavior. She leverages behavioural and neuroimaging methods, computational modeling and machine learning approaches to answer these questions.
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