Datasets for Neuroinformatics Teaching Datasets for Neuroinformatics Teaching Neuroscience teaching traditionally involves lectures about physiology, anatomy, and cell signaling; computational neuroscience focuses on theory and simulation. Rarely are real experimental datasets used in neuroscience teaching. Yet engagement with real data is key to a practical understanding of neuroscience concepts. Two obstacles prevents this engagement. One is the availability of datasets simple enough for students to grasp, but rich enough to communicate key analysis concepts. For example, a patch clamp data set should contain only the essential recorded traces, but enough to allow students to calculate FI curves, PSTHs, and other key electrophysiology analysis outputs. The second obstacle is that students require an analysis pipeline that is guaranteed to work if simple instructions are followed; these instructions must be accessible to students of all backgrounds. Analyses must be based on a stack of common, open, well-documented tools whose applicability-without-error can be continuously verified. I propose to acquire from the community ten datasets across a range of experimental techniques (at least intracellular recordings, extracellular recordings, calcium imaging, intrinsic signal imaging, fMRI, EEG, and morphological reconstruction), filter/pare these datasets to meet the above criteria, and create a set of reproducible analyses with instructions that undergraduate or graduate students can use to learn the corresponding analysis concepts.
|Effective start/end date||7/15/18 → 7/14/19|
- International Neuroinformatics Coordinating Facility (INCF): $7,745.00
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