NWB

Standardized format for neuroscience data sharing and archival

Open Science & Data Sharing Intermediate Data Format Recommend Provisional Not currently taught

Our View

Recommend Provisional Not currently taught

NWB is the emerging standard for neuroscience data sharing, required by many NIH BRAIN Initiative grants. It provides a unified format for diverse data types (ephys, imaging, behavior) with rich metadata, enabling tool interoperability and compliance with FAIR principles.

Provisional positions are editorial proposals awaiting group review.

Not currently taught — Relevant to the topic landscape; no explicit recorded offering is currently attributed to this item.

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Related themes: Neural Data Analysis

Common use cases

Multi-modal neuroscience experiments
Data sharing via DANDI Archive
Cross-institutional collaboration
NIH BRAIN Initiative compliance

Getting started

Neurodata Without Borders (NWB) is a data standard for neurophysiology, providing a unified format for storing diverse neuroscience data types with rich metadata. Built on HDF5, NWB enables data sharing, reproducibility, and tool interoperability.

Key Features

Standardized schema: Consistent structure for common neuroscience data types
Comprehensive coverage: Supports electrophysiology, imaging, behavior, optogenetics, and more
Rich metadata: Stores experimental protocols, subject information, and device specifications
Tool ecosystem: Growing collection of analysis tools and visualization software
FAIR principles: Designed for findable, accessible, interoperable, reusable data

Data Types Supported

Electrophysiology: Spike times, LFP, intracellular recordings
Optical physiology: Two-photon calcium imaging, fiber photometry
Behavior: Trials, stimuli, behavioral events, video tracking
Optogenetics: Stimulation parameters and timing
Anatomical images: MRI, histology, fluorescence microscopy

Python Integration

from pynwb import NWBFile, NWBHDF5IO
from pynwb.ecephys import LFP, ElectricalSeries
from datetime import datetime
from dateutil.tz import tzlocal

# Create NWB file
nwbfile = NWBFile(
    session_description='Visual stimulus experiment',
    identifier='mouse_01_session_001',
    session_start_time=datetime.now(tzlocal()),
    experimenter=['Smith, J.'],
    lab='iBOTS Lab',
    institution='University',
    experiment_description='V1 responses to drifting gratings',
    subject={
        'subject_id': 'M01',
        'age': 'P90D',
        'species': 'Mus musculus',
        'sex': 'M',
    }
)

# Add electrode information
device = nwbfile.create_device(
    name='Neuropixels 1.0',
    description='384-channel probe',
    manufacturer='IMEC'
)

electrode_group = nwbfile.create_electrode_group(
    name='V1',
    description='Primary visual cortex',
    location='V1',
    device=device
)

# Add electrodes
for i in range(64):
    nwbfile.add_electrode(
        id=i,
        x=0.0, y=0.0, z=float(i * 20),
        imp=float('nan'),
        location='V1',
        filtering='300-6000 Hz',
        group=electrode_group
    )

# Add LFP data
electrode_table_region = nwbfile.create_electrode_table_region(
    region=list(range(64)),
    description='recorded electrodes'
)

lfp_electrical_series = ElectricalSeries(
    name='LFP',
    data=lfp_data,  # shape: (n_time, n_channels)
    electrodes=electrode_table_region,
    starting_time=0.0,
    rate=1000.0,
    description='Local field potential'
)

lfp = LFP(electrical_series=lfp_electrical_series)
ecephys_module = nwbfile.create_processing_module(
    name='ecephys',
    description='Processed electrophysiology data'
)
ecephys_module.add(lfp)

# Write to file
with NWBHDF5IO('experiment.nwb', 'w') as io:
    io.write(nwbfile)

# Read from file
with NWBHDF5IO('experiment.nwb', 'r') as io:
    read_nwbfile = io.read()
    lfp_data = read_nwbfile.processing['ecephys']['LFP']['LFP'].data[:]

When to Use NWB

Best for:

Multi-modal neuroscience experiments
Data intended for public sharing (e.g., DANDI Archive)
Labs collaborating across institutions
Long-term data archival
Compliance with funding requirements (NIH BRAIN Initiative)

Consider alternatives for:

Simple time-series analysis (use Parquet or HDF5)
Custom data types not covered by NWB schema
Rapid prototyping before standardization

Integration with Ecosystem

NWB integrates with major neuroscience tools:

SpikeInterface: Spike sorting with NWB I/O
Suite2p/CaImAn: Calcium imaging analysis
DeepLabCut: Pose estimation tracking
DANDI Archive: Cloud storage for NWB datasets
NWB Explorer: Web-based visualization