All NWB conversions

Mala Murthy

Princeton University

Project started August 2022

Developed NWB conversion tools for the Murthy lab's Drosophila neuroscience datasets. The conversion pipeline handles complex behavioral and neural data, including calcium imaging of visual neurons during courtship behavior, processed SLEAP joint position tracks, and song labels, supporting research into neural circuits underlying social behavior in fruit flies.

Data and methods
Drosophilacalcium imagingbehavioral trackingsocial behaviorvisual processing
Conversion code
murthy-lab-to-nwb
Published datasets
Funded under
SCGB NWB Adoption
Related work

Carlos Brody

Princeton University

Developed NWB conversion tools for the Brody lab's neurophysiology datasets focused on decision-making and neural circuits. The project includes custom processing pipelines and utility code for converting experimental data to the standardized NWB format, with specialized Python-based conversion tools.

David Tank

Princeton University

Developed comprehensive NWB conversion tools for the Tank lab's behavioral and electrophysiology datasets. Created a custom NWB extension (ndx-tank-metadata) for storing experiment-specific metadata including maze configurations and rig parameters. The conversion pipeline integrates Neuropixels recordings with VirMen behavioral data, including detailed trial structure, behavioral metrics, and synchronized TTL events. The tools support extensive metadata handling and include specialized visualization widgets for behavioral analysis.

Beth Buffalo

University of Washington

Developed NWB conversion tools for the Buffalo lab's multi-modal neurophysiology data, handling both raw and processed neural recordings from Neuralynx systems, sorted spikes, and behavioral measurements in non-human primates doing hippocampal tasks. The conversion pipeline includes a graphical interface for metadata editing and supports various data formats including NCS, MAT, and NEX files.

Bob Datta

Harvard University

Developed NWB conversion tools for the Datta lab's behavioral neuroscience datasets, featuring photometric recordings and optogenetic manipulations in mice. The conversion pipeline handles multi-modal data including fiber photometry signals, behavioral measurements, and closed-loop optogenetic stimulation data studying dopamine dynamics during spontaneous behavior.