# Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)

## TL;DR

The paper introduces NEAO, an ontology to standardize and clarify the description of neuroelectrophysiology data analysis processes.

## Contribution

The novel contribution is the development of the Neuroelectrophysiology Analysis Ontology (NEAO) to standardize analysis descriptions and improve knowledge sharing.

## Key findings

- NEAO provides a standardized vocabulary for describing neuroelectrophysiology data analysis processes.
- Real-world examples show how NEAO can annotate and clarify the provenance of analysis results.
- The ontology supports querying and reusing analysis information via knowledge graphs.

## Abstract

Describing the analysis of data from electrophysiology experiments investigating the function of neural systems is challenging. On the one hand, data can be analyzed by distinct methods with similar purposes, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same analysis algorithm, while adopting different names to identify functions and parameters. These ambiguities complicate reporting analysis results, e.g., in a manuscript or on a scientific platform. Here, we illustrate how an ontology to describe the analysis process can assist in improving clarity, rigour and comprehensibility by complementing, simplifying and classifying the details of the implementation. We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a vocabulary and to standardize the descriptions of processes for neuroelectrophysiology data analysis. Real-world examples demonstrate how NEAO can annotate provenance information describing an analysis. Based on such provenance, we detail how it supports querying information (e.g., using knowledge graphs) that enable researchers to find, understand and reuse analysis results.