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| 001 | 892654 | ||
| 005 | 20250321120424.0 | ||
| 037 | _ | _ | |a FZJ-2021-02240 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a NeuroFrance 2021 |c Online |d 2021-05-19 - 2021-05-21 |w Online |
| 245 | _ | _ | |a Orchestrating analysis workflows using Elephant and Neo |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1636121450_6647 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 520 | _ | _ | |a In order to deal with the increasing complexity of data from electrophysiological experiments and spiking neural network simulations, concepts and tools to perform data acquisition and analysis in a reproducible fashion are in high demand. Here, following [1], we demonstrate open-source software solutions that support such workflows, each addressing different aspects of the process: (i) electrophysiological data of different origins are represented in a standard description using Neo (RRID:SCR_000634) [2], (ii) complex metadata accumulating in the electrophysiological experiment [3] are organized [4] using the open metadata markup language (odML, RRID:SCR_001376) [5], and (iii) analysis is performed using the Electrophysiology Analysis Toolkit (Elephant, RRID:SCR_003833, http://python-elephant). Elephant acts as the central modular software component that provides generic library functions to perform standard and advanced analysis methods for parallel, multi-scale activity data. We outline how the integration of such workflows into the EBRAINS infrastructure facilitates interdisciplinary, collaborative work including access to high-performance computing. In particular, we demonstrate how such tools form the basis for rigorous approaches to model validation [6]. |
| 536 | _ | _ | |a 523 - Neuromorphic Computing and Network Dynamics (POF4-523) |0 G:(DE-HGF)POF4-523 |c POF4-523 |f POF IV |x 0 |
| 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 1 |
| 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 2 |
| 536 | _ | _ | |a HAF - Helmholtz Analytics Framework (ZT-I-0003) |0 G:(DE-HGF)ZT-I-0003 |c ZT-I-0003 |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 HMC - Helmholz Metadata Collaboration ((DE-HGF)HMC_20200306) |0 G:(DE-HGF)HMC_20200306 |c (DE-HGF)HMC_20200306 |x 5 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 856 | 4 | _ | |u https://www.professionalabstracts.com/nf2021/iplanner/#/presentation/275 |
| 909 | C | O | |o oai:juser.fz-juelich.de:892654 |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)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 |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)POF4-5235 |x 1 |
| 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 0 |
| 914 | 1 | _ | |y 2021 |
| 920 | _ | _ | |l no |
| 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 abstract |
| 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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