001041471 001__ 1041471
001041471 005__ 20250416202206.0
001041471 0247_ $$2doi$$a10.48550/ARXIV.2412.05021
001041471 037__ $$aFZJ-2025-02264
001041471 1001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano$$b0$$eCorresponding author$$ufzj
001041471 245__ $$aImproving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)
001041471 260__ $$barXiv$$c2024
001041471 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1744789638_17200
001041471 3367_ $$2ORCID$$aWORKING_PAPER
001041471 3367_ $$028$$2EndNote$$aElectronic Article
001041471 3367_ $$2DRIVER$$apreprint
001041471 3367_ $$2BibTeX$$aARTICLE
001041471 3367_ $$2DataCite$$aOutput Types/Working Paper
001041471 520__ $$aDescribing the processes involved in analyzing data from electrophysiology experiments to investigate the function of neural systems is inherently challenging. On the one hand, data can be analyzed by distinct methods that serve a similar purpose, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same algorithm for the analysis while adopting different names to identify functions and parameters. Having reproducibility in mind, with these ambiguities the outcomes of the analysis are difficult to report, e.g., in the methods section of a manuscript or on a platform for scientific findings. Here, we illustrate how using an ontology to describe the analysis process can assist in improving clarity, rigour and comprehensibility by complementing, simplifying and classifying the details of the implementation. We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a unified vocabulary and to standardize the descriptions of the processes involved in analyzing data from neuroelectrophysiology experiments. Real-world examples demonstrate how the NEAO can be employed to annotate provenance information describing an analysis process. Based on such provenance, we detail how it can be used to query various types of information (e.g., using knowledge graphs) that enable researchers to find, understand and reuse prior analysis results.
001041471 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001041471 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001041471 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x2
001041471 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x3
001041471 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001041471 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x5
001041471 588__ $$aDataset connected to DataCite
001041471 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001041471 650_7 $$2Other$$aFOS: Biological sciences
001041471 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b1$$ufzj
001041471 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b2$$ufzj
001041471 773__ $$a10.48550/ARXIV.2412.05021$$p2412.05021$$tarXiv$$y2024
001041471 909CO $$ooai:juser.fz-juelich.de:1041471$$popenaire$$pVDB$$pec_fundedresources
001041471 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180365$$aForschungszentrum Jülich$$b0$$kFZJ
001041471 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)180365$$aRWTH Aachen$$b0$$kRWTH
001041471 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b1$$kFZJ
001041471 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)144168$$aRWTH Aachen$$b1$$kRWTH
001041471 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144807$$aForschungszentrum Jülich$$b2$$kFZJ
001041471 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001041471 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001041471 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
001041471 980__ $$apreprint
001041471 980__ $$aVDB
001041471 980__ $$aI:(DE-Juel1)IAS-6-20130828
001041471 980__ $$aI:(DE-Juel1)INM-10-20170113
001041471 980__ $$aUNRESTRICTED