Many different software tools are used to acquire and analyze electrophysiology data, and each tool typically has its own proprietary file format, making sharing and reusing data difficult. To improve data management within the neuroscience community, a good approach is the conversion of electrophysiology data to open, standard formats compatible with FAIR principles. Two such formats, endorsed as community standards by INCF, are (i) Neurodata Without Borders: Neurophysiology (NWB:N) and (ii) Neuroscience Information eXchange (NIX).
For the purpose of converting data from proprietary or otherwise non-standard formats, Neo, a Python library providing a common representation of electrophysiology and optophysiology data and with support for reading and writing a wide range of neurophysiology file formats, is well suited. A particular challenge is ensuring that all of the necessary metadata is stored in the standard format alongside the raw data, and Neo’s flexible system of annotations is a helpful tool for addressing this.
In this poster we present the general data conversion pipelines as well as specific case studies.