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

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-05-29 DOI:10.1038/s41597-025-05213-3

Cristiano A Köhler, Sonja Grün, Michael Denker

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引用次数: 0

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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通过神经电生理分析本体（NEAO）改进数据共享和知识转移。

描述对研究神经系统功能的电生理学实验数据的分析是具有挑战性的。一方面，可以用不同的方法来分析数据，但目的相似，例如用不同的算法来估计被测时间序列的谱功率含量。另一方面，不同的软件代码可以实现相同的分析算法，而采用不同的名称来标识函数和参数。这些模糊性使报告分析结果复杂化，例如，在手稿或科学平台上。在这里，我们将说明描述分析过程的本体如何通过补充、简化和分类实现细节来帮助提高清晰度、严谨性和可理解性。我们实现了神经电生理分析本体（NEAO）来定义词汇表并标准化神经电生理数据分析过程的描述。真实世界的例子演示了NEAO如何注释描述分析的来源信息。基于这种来源，我们详细说明了它如何支持查询信息（例如，使用知识图），使研究人员能够找到、理解和重用分析结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.