Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05213-3 |
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| author | Cristiano A. Köhler Sonja Grün Michael Denker |
| author_facet | Cristiano A. Köhler Sonja Grün Michael Denker |
| author_sort | Cristiano A. Köhler |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4af5287abf924c20be16fb7d1d5d2ac8 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4af5287abf924c20be16fb7d1d5d2ac82025-08-20T03:16:52ZengNature PortfolioScientific Data2052-44632025-05-0112113110.1038/s41597-025-05213-3Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)Cristiano A. Köhler0Sonja Grün1Michael Denker2Institute for Advanced Simulation (IAS-6), Jülich Research CentreInstitute for Advanced Simulation (IAS-6), Jülich Research CentreInstitute for Advanced Simulation (IAS-6), Jülich Research CentreAbstract 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.https://doi.org/10.1038/s41597-025-05213-3 |
| spellingShingle | Cristiano A. Köhler Sonja Grün Michael Denker Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) Scientific Data |
| title | Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| title_full | Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| title_fullStr | Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| title_full_unstemmed | Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| title_short | Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| title_sort | improving data sharing and knowledge transfer via the neuroelectrophysiology analysis ontology neao |
| url | https://doi.org/10.1038/s41597-025-05213-3 |
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