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001044241 1001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano A.$$b0$$eCorresponding author
001044241 245__ $$aImproving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)
001044241 260__ $$aLondon$$bNature Publ. Group$$c2025
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001044241 520__ $$aDescribing the analysis of data from electrophysiology experiments investigating the function of neural systems is challenging. On the one hand, data can be analyzed by distinct methods with similar purposes, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same analysis algorithm, while adopting different names to identify functions and parameters. These ambiguities complicate reporting analysis results, e.g., in a manuscript or on a scientific platform. Here, we illustrate how an ontology to describe the analysis process can assist in improving clarity, rigour and comprehensibility by complementing, simplifying and classifying the details of the implementation. We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a vocabulary and to standardize the descriptions of processes for neuroelectrophysiology data analysis. Real-world examples demonstrate how NEAO can annotate provenance information describing an analysis. Based on such provenance, we detail how it supports querying information (e.g., using knowledge graphs) that enable researchers to find, understand and reuse analysis results.
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001044241 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b1$$ufzj
001044241 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b2
001044241 773__ $$0PERI:(DE-600)2775191-0$$a10.1038/s41597-025-05213-3$$gVol. 12, no. 1, p. 907$$n1$$p907$$tScientific data$$v12$$x2052-4436$$y2025
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