Reproducibility and efficiency in handling complex neurophysiological data
Author:
Denker Michael1ORCID, Grün Sonja12ORCID, Wachtler Thomas3ORCID, Scherberger Hansjörg45ORCID
Affiliation:
1. Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10) , Jülich Research Centre , Jülich , Germany 2. Theoretical Systems Neurobiology, RWTH Aachen University , Aachen , Germany 3. Department Biologie II , Ludwig-Maximilians-Universität München , Planegg-Martinsried , Germany 4. Neurobiology Laboratory , Deutsches Primatenzentrum GmbH , Kellnerweg 4 , 37077 Göttingen , Germany 5. Department of Biology and Psychology , University of Goettingen , Goettingen , Germany
Abstract
Abstract
Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.
Funder
Horizon 2020 Framework Programme Deutsche Forschungsgemeinschaft Bundesministerium für Bildung und Forschung
Publisher
Walter de Gruyter GmbH
Subject
Clinical Neurology,Neurology
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