McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, United States
Lawrence Niu
MBF Bioscience, Ashburn, United States
Pamela Baker
Allen Institute for Brain Science, Seattle, United States
Ivan Soltesz
Department of Neurosurgery, Stanford University, Stanford, United States
Lydia Ng
Allen Institute for Brain Science, Seattle, United States
Karel Svoboda
Allen Institute for Brain Science, Seattle, United States; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
Loren Frank
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States; Kavli Institute for Fundamental Neuroscience, San Francisco, United States; Departments of Physiology and Psychiatry University of California, San Francisco, San Francisco, United States
Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, United States; Kavli Institute for Fundamental Neuroscience, San Francisco, United States; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, United States; Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, United States; Weill Neurohub, Berkeley, United States
The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.
