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| Home > Publications database > Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) > print |
| 001 | 1041471 | ||
| 005 | 20250416202206.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2412.05021 |2 doi |
| 037 | _ | _ | |a FZJ-2025-02264 |
| 100 | 1 | _ | |a Köhler, Cristiano |0 P:(DE-Juel1)180365 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO) |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1744789638_17200 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Describing 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. |
| 536 | _ | _ | |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) |0 G:(DE-HGF)POF4-5235 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 2 |
| 536 | _ | _ | |a Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) |0 G:(DE-Juel-1)iBehave-20220812 |c iBehave-20220812 |x 3 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 4 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 5 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Quantitative Methods (q-bio.QM) |2 Other |
| 650 | _ | 7 | |a FOS: Biological sciences |2 Other |
| 700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 1 |u fzj |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 2 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2412.05021 |p 2412.05021 |t arXiv |y 2024 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1041471 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)180365 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 0 |6 P:(DE-Juel1)180365 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)144168 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 1 |6 P:(DE-Juel1)144168 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)144807 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5235 |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
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