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Updated: May 29, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
Published on: June 5, 2017
Enhancing FAIRdata by providing digital workflows from data generation to the publication of data: an open source
David Herrmann1, Patrick Hodapp2, Martin Starman1
1Institute of Biological and Chemical Systems, Functional Molecular Systems (IBCS), Karlsruhe Institute of Technology Kaiserstraße 12 76131 Karlsruhe Germany nicole.jung@kit.edu stefan.braese@kit.edu https://ror.org/04t3en479.
Abstract:
Analytical data in chemistry and other disciplines is usually generated in different formats and lacks common data and metadata standards that are necessary for a FAIR handling of research data. In the work presented herein, we describe a workflow that uses non-standardized, in some cases proprietary, data formats from cyclic voltammetry measurements coming from individual devices as an instructive example, to yield open, standardized data that are annotated with rich metadata. The presented workflow includes concepts, software and infrastructure that can be used to support the whole data life cycle from the measurement of data to the publication of data and metadata in repositories. Components used for this workflow were made available as open source, allowing the re-use of this approach in other laboratories. The methods described for cyclic voltammetry can be adapted and used for other measurements and experimental data collections, allowing for an easy way to integrate new methods for digitalized research and FAIR data management.
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