RohdatenWorkflows

RohdatenWorkflows are end-to-end pipelines designed to manage and process raw data from acquisition to analysis-ready results, with a focus on data provenance, reproducibility, and governance. They integrate steps for data ingestion, metadata capture, quality control, preprocessing, normalization, transformation, feature extraction, and storage of intermediate results, documenting each step for traceability.

They rely on workflow-management systems and containerization to ensure portability and reproducibility. Common components include parameter and configuration management, version control, automatic logging, error handling, and data lineage tracking. In practice, RohdatenWorkflows emphasize modular design and reproducible environments, often employing standardized file formats and metadata schemas.

Applications span multiple domains, including bioinformatics (such as raw sequencing reads and derived variants), medical imaging, remote sensing, and experimental physics. The workflows handle large data volumes, heterogeneous formats, and privacy or access-control requirements, making data governance an integral part of design.

Challenges include maintaining scalability, ensuring metadata completeness, controlling data access, and validating each processing step to prevent error propagation. Best practices entail clear documentation, rigorous testing with reference datasets, use of containerized environments, automated backups, and adherence to community standards for metadata and provenance.