Journal Article

FZJ-2025-03129

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)

Köhler, C. A. (Corresponding author)FZJ*RWTH* ; Grün, S.FZJ*RWTH* ; Denker, M.FZJ*

2025
Nature Publ. Group London

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Please use a persistent id in citations: doi:10.1038/s41597-025-05213-3  doi:10.34734/FZJ-2025-03129

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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (IAS-6)
2. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) (POF4-523)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) (101147319)
4. Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) (iBehave-20220812)
5. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)
6. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)
7. DFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

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