SBP Review Neo

Le résumé fourni par la source

The purpose of this document is to solicit community feedback on Neo, an object model for representing electrophysiology and optophysiology data that was submitted to INCF for endorsement as a standard. The document contains the INCF standards and best practices committee's review of Neo, and the criteria in which it was evaluated (open, FAIR, testing and implementation, governance, adoption and use, stability and support, and comparison to similar standards). For the next 60 days, we are seeking community feedback on Neo. About Neo: Neo is a common, shared object model for representing electrophysiology and optophysiology data, with the goal of improving interoperability between tools for analyzing, visualizing and generating electrophysiology data (such as Elephant, ephyviewer, PyNN). The Python implementation of the object model, distributed as the Neo Python package, has support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including NWB and NIX. In order to be as lightweight a dependency as possible, Neo is deliberately limited to representation of data, with no functions for data analysis or visualization. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes) and image sequences. To take into account the heterogeneity of scenarios under which data are acquired in experiment and simulation, Neo features a flexible representation of structured relationships between individual data objects and supports the annotation of data objects with arbitrary metadata as key-value pairs. In Python, Neo's data objects include robust handling of physical units (via the quantities package), including unit conversions. They can be used as NumPy arrays from a user perspective to simplify their use and integration in existing code.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.