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| Home > Publications database > FAIRification of electrophysiology data analysis: provenance capture in the Elephant toolbox > print |
| 001 | 894266 | ||
| 005 | 20240313094852.0 | ||
| 037 | _ | _ | |a FZJ-2021-03141 |
| 100 | 1 | _ | |a Köhler, Cristiano |0 P:(DE-Juel1)180365 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a INCF Neuroinformatics Assembly 2021 |c online |w online |
| 245 | _ | _ | |a FAIRification of electrophysiology data analysis: provenance capture in the Elephant toolbox |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Talk (non-conference) |b talk |m talk |0 PUB:(DE-HGF)31 |s 1628514494_30837 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a Other |2 DINI |
| 500 | _ | _ | |a Contribution to the Human Brain Project Booth at the INCF Neuroinformatics Assembly 2021 |
| 520 | _ | _ | |a The analysis of electrophysiology data typically comprises multiple steps. These often consist of several scripts executed in a specific temporal order, which take different parameter sets and use distinct data files. As the researcher adjusts the individual analysis steps to accommodate new hypotheses or additional data, the resulting workflows may become increasingly complex, and undergo frequent changes. Although it is possible to use workflow management systems to organize the execution of the scripts and capture provenance information at the level of the script (i.e., which script file was executed, and in which environment?) and data file (i.e., which input and output files were supplied to that script), the resulting provenance track does not automatically provide details about the actual analysis carried out inside each script. Therefore, the final analysis results can only be understood by source code inspection or reliance in any accompanying documentation. We focus on two open-source tools for the analysis of electrophysiology data developed in EBRAINS. The Neo (RRID:SCR_000634) framework provides an object model to standardize neural activity data acquired from distinct sources. Elephant (RRID:SCR_003833) is a Python toolbox that provides several functions for the analysis of electrophysiology data. We set to improve these tools by implementing a data model that captures detailed provenance information and by representing the analysis results in a systematic and formalized manner. Ultimately, these developments aim to improve reproducibility, interoperability, findability, and re-use of 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 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 1 |
| 536 | _ | _ | |a 571 - Connectivity and Activity (POF3-571) |0 G:(DE-HGF)POF3-571 |c POF3-571 |f POF III |x 2 |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 3 |
| 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 4 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 5 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |x 6 |
| 536 | _ | _ | |a HAF - Helmholtz Analytics Framework (ZT-I-0003) |0 G:(DE-HGF)ZT-I-0003 |c ZT-I-0003 |x 7 |
| 909 | C | O | |o oai:juser.fz-juelich.de:894266 |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 |
| 913 | 0 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-571 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Connectivity and Activity |x 0 |
| 913 | 0 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Theory, modelling and simulation |x 1 |
| 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 |
| 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)POF4-5231 |x 1 |
| 914 | 1 | _ | |y 2021 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-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 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
| 980 | _ | _ | |a talk |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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