Enhancing FAIRdata by providing digital workflows from data generation to the publication of data: an open source approach described for cyclic voltammetry
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View abstract on PubMed
Summary
This summary is machine-generated.This study presents an open-source workflow to standardize diverse analytical data, like cyclic voltammetry, ensuring FAIR data principles. The adaptable methods support the entire data lifecycle for digitalized research.
Area Of Science
- Chemistry
- Data Science
- Scientific Research
Background
- Analytical data often exists in varied, non-standardized formats, hindering FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
- Lack of common data and metadata standards complicates research data management and sharing across disciplines.
Purpose Of The Study
- To develop and present a workflow for converting non-standardized, proprietary analytical data into open, standardized formats with rich metadata.
- To support the entire data lifecycle, from measurement to publication in repositories, promoting FAIR data handling.
- To provide open-source tools and infrastructure for reproducible and accessible scientific data management.
Main Methods
- Utilized cyclic voltammetry measurements as an example case for data format standardization.
- Developed a workflow incorporating concepts, software, and infrastructure for data processing and metadata annotation.
- Made all workflow components available as open-source to facilitate adoption and reuse.
Main Results
- Successfully transformed non-standardized cyclic voltammetry data into open, standardized data annotated with comprehensive metadata.
- Established a complete data lifecycle support system, from data generation to repository publication.
- Created reusable open-source components for data management.
Conclusions
- The presented workflow effectively addresses the challenge of diverse analytical data formats, enabling FAIR data principles.
- The open-source nature of the workflow promotes its adoption and adaptation in various laboratories and for different measurement types.
- This approach facilitates digitalized research and enhances data management practices for scientific data collections.
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