[New Tutorial] Annotate, prepare tests and publish on workflow registries Galaxy workflows #6119

clsiguret · 2025-05-23T06:12:37Z

Hello 😄

With @bebatut and @nagoue , we have written a new tutorial on workflow FAIRification in Galaxy.

We got the idea while working on the Hackathon MicroGalaxy IWC event.

We will be presenting this tutorial at the "Printemps de la donnée" event next week.

Thanks a lot! 😃

…t/training-material into workflow_fairification-tutorial

bgruening · 2025-05-23T07:24:07Z

topics/galaxy-interface/tutorials/workflow-fairification/images/journal.pcbi.1011369.g001.PNG

Is it possible to also add an SVG so we can adjust the image in the future?

This image comes directly from the journal. We do not have SVG

We can still adjust the display size in the tutorial itself

I don't see any svg format in the publication 😬

bgruening

This is so nice. Thanks a lot. A few comments inline.

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

bgruening · 2025-05-23T07:27:19Z

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

+
+Research data is accumulating at an unprecedented rate, presenting significant challenges for achieving fully reproducible science. As a result, implementing high-quality management of scientific data has become a global priority. One key aspect of this effort is the use of computational workflows, which describe the complex, multi-step methods used for data analysis. In Galaxy, workflows are a powerful feature that allows researchers to link multiple steps of complex analyses seamlessly. To maximize their impact, these workflows should adhere to best practices that make them **FAIR: Findable, Accessible, Interoperable, and Reusable**.
+
+The FAIR principles —-Findable, Accessible, Interoperable, and Reusable—- provide practical guidelines for enhancing the value of research data:


maybe link to our FAIR trainings?

Added a comment box below

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

bgruening · 2025-05-23T07:30:05Z

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

+
+# Prepare the workflow
+
+Here, we will use a workflow running FastQC and MultiQC but you can use your own workflow.


Can we use Falco instead of FastQC? Or Fastp?

Good point. I will change in the tutorial

Can Falco output be used in MultiQC? I can not find Falco in the list of tools in MultiQC

Falco is a drop-in replacement for FastQC, you can use FastQC module of MQC

Will try then

bgruening · 2025-05-23T07:33:12Z

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

+> {: .solution}
+{: .question}
+
+Galaxy workflow interface supports some metadata. Are they enough to fulfill Tip 2 and also Tip 1 *"Register the workflow."*. WorkflowHub, a workflow registry we will explain more later, supports the [following metadata](https://about.workflowhub.eu/docs/metadata-list/):


Galaxy workflow interface supports some metadata. Are they enough to fulfill Tip 2 and also Tip 1 "Register the workflow.".

Is that a question?

bgruening · 2025-05-23T07:40:16Z

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

+- Tip 4 (**Accessibility**): *"Provide example input data and results along with the workflow."*
+- Tip 10 (**Reusability**): *"Provide clear and concise workflow documentation."*
+
+   The workflow annotation provides a short and concise description of the workflow. So Tip 10 is partially fullfilled. In addition to the workflow annotation, each step could annotated so users could have an idea about the purpose of the steps


Can we mention here the fancy new workflow annotations from Laila as well? markdown inside the workflow, color encoded separations etc ... they are Galaxy specific but are imho also enhancing the documentation.

Good idea. Where do you have documentation for that?

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

bgruening · 2025-05-23T07:51:42Z

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

+
+### Find input datasets
+
+To test the workflow, we need input datasets. By fulfilling Tip 4 (*"Provide example input data and results along with the workflow."*), we generated a toy dataset. We now need to publish it to [**Zenodo**](https://zenodo.org/) to have a permanent URL, also allowing others to **easily retrieve and reuse the data** when running or validating the workflow.


In https://github.com/galaxyproject/training-material/pull/6119/files#diff-73ef156f75f9e4388f6473b70c13db1b5d26115d1f1007db168182ad6e858725R335 we already had test data?

Should we move this paragraph up?

topics/galaxy-interface/tutorials/workflow-fairification/tutorial.md

Co-authored-by: Björn Grüning <[email protected]>

clsiguret and others added 11 commits March 24, 2025 16:20

Init 'Workflow FAIRification' GTN

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init Best practices part

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init Create test data part

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init Conclusion part

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init Zenodo and IWC parts

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Add questions, objectives and key points

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Expand introduction

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Expand introduction

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Correct error in .md

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Merge branch 'workflow_fairification-tutorial' of github.com:clsigure…

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…t/training-material into workflow_fairification-tutorial

Expand tutorial

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clsiguret requested review from jennaj, Delphine-L and teresa-m as code owners May 23, 2025 06:12

github-actions bot added galaxy-interface faqs labels May 23, 2025

bgruening reviewed May 23, 2025

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Co-authored-by: Björn Grüning <[email protected]>


		Research data is accumulating at an unprecedented rate, presenting significant challenges for achieving fully reproducible science. As a result, implementing high-quality management of scientific data has become a global priority. One key aspect of this effort is the use of computational workflows, which describe the complex, multi-step methods used for data analysis. In Galaxy, workflows are a powerful feature that allows researchers to link multiple steps of complex analyses seamlessly. To maximize their impact, these workflows should adhere to best practices that make them FAIR: Findable, Accessible, Interoperable, and Reusable.

		The FAIR principles —-Findable, Accessible, Interoperable, and Reusable—- provide practical guidelines for enhancing the value of research data:


		# Prepare the workflow

		Here, we will use a workflow running FastQC and MultiQC but you can use your own workflow.


		### Find input datasets

		To test the workflow, we need input datasets. By fulfilling Tip 4 ("Provide example input data and results along with the workflow."), we generated a toy dataset. We now need to publish it to [Zenodo](https://zenodo.org/) to have a permanent URL, also allowing others to easily retrieve and reuse the data when running or validating the workflow.

[New Tutorial] Annotate, prepare tests and publish on workflow registries Galaxy workflows #6119

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[New Tutorial] Annotate, prepare tests and publish on workflow registries Galaxy workflows #6119

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clsiguret commented May 23, 2025

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