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NWB: Neurophysiology

Unlike in other fields (i.e. genetics and cell biology), neuroscience does not have a standardized way to collect and share the wealth of existing data among researchers. The lack of a common format has made comparison across laboratories difficult and replication of specific experiments almost impossible, significantly slowing overall progress in the field.

Launched in mid 2014, Neurodata Without Borders: Neurophysiology is a pilot project to produce a unified data format for cellular-based neurophysiology data. The common data format is based on representative use case studies from four laboratories, and the project includes a vetting phase for assessing whether other data models can also be used in the new common format.

The project will share not only the NWB format, but also high-value data sets that have been translated into the new format, as well as a collection of application programming interfaces (APIs) for reading and writing the data.

Neurodata Without Borders: Neurophysiology is intended to serve the broad neuroscience community and encourage the sharing of data by scientists worldwide.

The NWB Format

The resulting “NWB” format is designed to be flexible enough to incorporate several kinds of data, including electrophysiological and optical physiology data, and to include complex metadata related to stimuli and behavior.

A beta version of the format is now available, please give it a try.  The new NWB v2.0 will be released at SfN in November 2017. Join our mailing list for updates or ask questions on our google group.

To learn more about the approach taken to develop the NWB Format, please read our open access Neuron NeuroView article.

Goals & Values of the NWB Format

Improve data presentation and distribution.

To achieve scientific breakthroughs, today’s increasingly complex scientific findings require effortless dissemination of new data and appropriate tools to view and utilize that data (i.e. APIs, software). The presentation, packaging and distribution of large, complex data sets, particularly the high-dimensional datasets coming out of modern neurophysiology experiments, must be less regimented and more nimble. A common data format is a requirement for facilitating this process.

Support cross-validation and reproducibility.

Often key results in the field cannot be reproduced. Reexamination of the original data, facilitated by a common data format, can clear up confusion and minimize uncertainties.

Encourage best practices.

Well-considered procedures for collecting and storing the relevant metadata will be designed and implemented.

Facilitate and expedite discovery.

Experimentalists are often not equipped to extract and interpret all, or even the most important, meanings from the mountains of data being collected. Pooling and sharing the massive and complex data sets being generated today will enable scientists to expedite analyses and produce discoveries more efficiently. facilitated This will be enabled by a common data format.

Share analysis tools.

Many laboratories are developing powerful analysis software. A major impediment to share these tools has been incompatibilities between data models, a problem resolved by a common data format.

Create vital new collaborations with other fields.

A common data format will enable computational researchers and developers of new data mining techniques to seamlessly enter and participate in neuroscience research.

History

The initial development of NWB:N was funded by industry and private foundations. Scientific partners include the Allen Institute for Brain Science (AIBS), the Svoboda Lab (Janelia), the Meister Lab (Caltech), the Buzsáki Lab (NYU), and Fritz Sommers/Jeff Teeters (UCB, maintainers of CRCNS.org). This one year pilot project resulted in NWB:N 1.0. AIBS has adopted the use of this format and to date has released numerous NWB:N files assaying mouse visual cortex. NWB:N has also seen steady adoption by individual labs, including the Bouchard Lab (LBNL/UCB), Svoboda lab, Meister lab, Frank lab (UCSF), Chang lab (UCSF) and others.

Building on the success of NWB:N 1.0, the project entered a second phase, with additional leadership by Dr. Bouchard (Lawrence Berkeley National Laboratory, LBNL), Dr. Loren Frank (UCSF) and Dr. Edward Chang (UCSF). NWB:N 2.0 has been redeveloped by scientific software engineers/computer scientists at LBNL, Dr. Oliver Rübel and Mr. Andrew Tritt. A beta version of NWB:N 2.0 will be released in the Fall 2017, to coincide with the Society for Neuroscience conference.

The NWB:N project has also created a governance structure. An Executive Board (EB) decides the vision and roadmap for NWB:N and manages fundraising and outreach. The EB also works closely with a Technical Advisory Board (TAB), which makes technical decisions and maintains the software ecosystem.

A beta version of the format was released in 2016, the new NWB v2.0 will be released at SfN in November 2017.

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