Version Control, Publishing & Validation of Workflows
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Version control is vital in reproducibility since it helps track changes you or contributors make to your code and documentation. We suggest using GitHub to host your workflows in an open access repository so that the research community can benefit from your work, and your work can benefit from feedback from the research community. Below, find steps for getting started with GitHub :
Upload your descriptor file (workflow), parameter files, and source code to a GitHub repository (see an )
We encourage users to publish their tools and workflows on Dockstore so that they can be used by the greater scientific community. Dockstore features allow users to build their pipelines to be open, reusable, and interoperable. Publishing your work in this way will enhance the value of your work and the resources available to the scientific community.
Here is how to get started sharing your work on Dockstore:
and link your account to external services, such as GitHub
Link your Dockstore account to your to display your scientific identity.
Create an , invite your collaborators, and promote your work in collections
Dockstore can help you create more accessible and transparent data science methods in your scientific publications. In this section, we want to provide some examples of FAIR workflows the community has shared.
We believe we can enhance the security and reusability of tools and workflows we share through open, community-driven best practices that exemplify the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles. We have established published in Dockstore. We ask that users try to implement these practices in the workflows that they share with the community.
In this , the researchers provided transparent methods by citing immutable DOI archives of their container-based workflows, and also shared a collection in the Broad Institute's organization on Dockstore. This collection includes several workflows, a README, and a link to a public workspace tutorial in the Terra cloud environment where users can learn exactly how to recreate their methods.
In this the authors shared their pipelines written in the Workflow Description Language in this collection on Dockstore, and created a where the community can recreate an exact analysis and figure from their publication.