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001017079 1001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano$$b0$$eCorresponding author$$ufzj
001017079 1112_ $$aBernstein Conference 2023$$cBerlin$$d2023-09-26 - 2023-09-29$$wGermany
001017079 245__ $$aGaining insight into the analysis of electrophysiology data: the Neuroelectrophysiology Analysis Ontology
001017079 260__ $$c2023
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001017079 520__ $$aElectrophysiology is frequently used to investigate brain function. The analysis of electrophysiology data requires specific transformations and methods of varying complexity, which makes the description of the processes involved and their results challenging. First, there are several variations of methods that can be applied to the data with similar purposes (e.g., different algorithms to compute the power spectral density from local field potentials), leading to multiple levels of granularity in the description. Second, a particular method can be implemented by different software codes (e.g., toolboxes such as Elephant [1] or MNE [2]; see also [3]) that adopt different names for the functions and the parameters used. In the end, this two-fold ambiguity leads to a situation where the outcome of an analysis is difficult to describe, and finding and comparing results based on such descriptions require expert knowledge and is hardly machine-actionable.An ontology defines the concepts within a domain without ambiguity (e.g., the exact method to compute a power spectral density) while providing relationships with semantic information (e.g., a grouping of spectral estimators). Therefore, the description of the processes used for the analysis of electrophysiology data with an ontology will allow their understanding and identification despite the method or implementation used. There are several ontologies in the biomedical sciences including a few for neuroscience and electrophysiology [4]. However, their level of description is limited to the data, metadata or general parts of the analysis workflow (e.g., experiments, subjects, equipment, and basic data input/output).We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a unified vocabulary and standardize the descriptions of the methods involved in the analysis of electrophysiology data. We show real-world examples where the NEAO was used to annotate the provenance information from different analyses of an electrophysiology dataset and highlight how it is possible to query information, facilitating finding and obtaining insights on the results (e.g., using knowledge graphs). In this way, NEAO identifies groups of similar methods while pointing to literature that informs of their differences. We demonstrate how NEAO can seamlessly integrate with Alpaca [5] to capture provenance information. This will help to represent the analysis results according to the FAIR principles [6].References:[1] Elephant (RRID:SCR_003833); Denker, M., Yegenoglu, A., Grün, S. (2018) Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 2018, P19. https://python-elephant.org [doi:10.12751/incf.ni2018.0019][2] MNE (RRID:SCR_005972); Gramfort, A. et al. (2013) MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience 7, 267. https://mne.tools [doi:10.3389/fnins.2013.00267][3] Unakafova, V.A., Gail, A. (2019) Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data. Frontiers in Neuroinformatics 13, 57. [doi:10.3389/fninf.2019.00057][4] NCBO BioPortal, https://bioportal.bioontology.org[5]  Alpaca (RRID:SCR_023739), https://alpaca-prov.readthedocs.io [6] Wilkinson, M.D. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 160018. [doi:10.1038/sdata.2016.18]
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001017079 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b1$$ufzj
001017079 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b2$$ufzj
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