Köhler Cristiano A, Grün Sonja, Denker Michael
Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany.
RWTH Aachen University, Aachen, Germany.
Sci Data. 2025 May 29;12(1):907. doi: 10.1038/s41597-025-05213-3.
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.
描述对研究神经系统功能的电生理实验数据的分析是具有挑战性的。一方面，数据可以通过具有相似目的的不同方法进行分析，例如使用不同算法来估计测量时间序列的频谱功率含量。另一方面，不同的软件代码可以实现相同的分析算法，同时采用不同的名称来标识函数和参数。这些模糊性使得报告分析结果变得复杂，例如在论文手稿或科学平台上。在这里，我们说明了一个用于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。实际示例展示了NEAO如何注释描述分析的来源信息。基于这样的来源，我们详细说明了它如何支持查询信息（例如使用知识图谱），使研究人员能够查找、理解和重用分析结果。
