Supporting NWB adoption for the Ripple U19 consortium
Through funding from the NIH BRAIN Initiative’s U19 program, we assist the Ripple U19 consortium in standardizing their neuroscience data using the NWB format. This project focuses on developing tools and workflows to convert and validate complex datasets from multiple labs studying hippocampal ripples and their role in memory consolidation.
The collaboration facilitates data sharing and analysis across the consortium’s member labs, enabling more effective cross-lab studies of hippocampal ripple events and their relationship to memory processes.
NWB conversions under this project
4 labs
Mark Schnitzer
Stanford University
Developed NWB conversion tools for the Schnitzer lab's in vivo calcium imaging datasets. The conversion pipeline handles data from miniaturized microscopes (miniscopes) and fiber photometry recordings, including behavioral measurements during freely moving animal experiments. The tools support both one-photon and two-photon calcium imaging data, with specialized interfaces for motion correction and cell segmentation outputs.
Attila Losonczy
Columbia University
Developed NWB conversion tools for the Losonczy lab's two-photon calcium imaging datasets. The conversion pipeline handles data from head-fixed mice running on voluntary treadmills, including raw ROI images, DfOverF traces, fluorescence traces, and behavioral measurements. The tools support molecular identification of interneuron subtypes and integrate position tracking with neural activity data.
Ivan Soltesz
Stanford University
Developed NWB conversion tools for the Soltesz lab's neuroscience datasets. The tools facilitate the standardization of experimental data into the NWB format, supporting the lab's research in neural circuit dynamics and computational neuroscience.
Gyorgy Buzsaki
New York University
Developed NWB conversion tools for the Buzsáki lab's extensive neurophysiology datasets, handling terabyte-scale data including Neuroscope recordings, LFP signals, and behavioral measurements. The conversion pipeline features specialized interfaces for various data types and supports parallel processing for large-scale conversions, with datasets publicly available through DANDI.