AI Article Synopsis
- Scientific workflows improve data analysis efficiency by automating tasks with various software in a specific sequence.
- Implementing FAIR principles is crucial for ensuring these workflows are transparent and reusable, as demonstrated in a case study using the Metabolome Annotation Workflow (MAW).
- MAW, defined in the Common Workflow Language (CWL), can operate on different engines and is packaged with metadata for better accessibility, serving as a guideline for researchers to adopt FAIR practices in their own workflows.
Article Abstract
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10891576 | PMC |
| http://dx.doi.org/10.3390/metabo14020118 | DOI Listing |
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