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Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by
Gerhard Mayer1,2,3, Wolfgang Müller4, Karin Schork1,2
1Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany.
Briefings in Bioinformatics
|February 16, 2021
Summary
This study assesses the FAIR data principles (findable, accessible, interoperable, reusable) within the de.NBI bioinformatics infrastructure. Challenges in implementing FAIR data are highlighted due to diverse services and distributed teams.
Area of Science:
- Bioinformatics
- Data Science
- Life Sciences
Background:
- The de.NBI infrastructure supports diverse bioinformatics services.
- Implementing FAIR data principles is crucial for research data management.
- Heterogeneity in services poses challenges to data standardization.
Purpose of the Study:
- To assess the FAIR status of de.NBI services.
- To identify challenges and requirements for FAIR data adherence.
- To inform research data management strategies in distributed infrastructures.
Main Methods:
- Case studies of de.NBI services.
- Self-assessments of FAIR data principles.
- Analysis of data, metadata, software, and workflow heterogeneity.
Main Results:
- FAIR compliance is challenging due to service heterogeneity.
- Distributed teams and numerous tools complicate FAIR implementation.
- A strong network of experts aids in developing data management plans.
Conclusions:
- Addressing heterogeneity is key to improving FAIR data adoption.
- Standardized approaches are needed for effective FAIR monitoring.
- The de.NBI infrastructure requires tailored strategies for robust FAIR data management.