FAIR Computational Workflows for Data Processing and Genomics

Computational workflows describe multi-step methods for data collection, preparation, analytics, and simulation that lead to new data products. These workflows inherently contribute to FAIR data principles by processing data with established metadata, creating metadata during processing, and tracking data provenance. The dataset, authored by Carole Goble of the University of Manchester, includes an example workflow for detecting variants in genome sequences specified in the Common Workflow Language.

Use Cases

• Designing reproducible data processing pipelines based on the description of workflows for data collection and preparation.
• Implementing FAIR data principles in analytics and simulation projects based on the workflow's inherent metadata creation and provenance tracking.
• Standardizing genomic analysis, such as variant detection, using Common Workflow Language descriptions for execution across different infrastructures.

Strengths

• Focuses on FAIR data principles, which are a recognized standard for data reuse and quality.
• Includes a concrete example of a genomics workflow for variant detection, demonstrating practical application.
• Workflows are specified in the Common Workflow Language, promoting standardization and execution across different systems.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file formats are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

University of Manchester

Collection Method

Likely compiled from research and examples of computational workflows.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

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