Boutiques tutorial
Opened this issue · 11 comments
Link to the repository:
https://github.com/boutiques/tutorial
Title of notebook:
Boutiques tutorial
Authors:
Tristan Glatard (@glatard), Greg Kiar (@gkiar)
Short layman description:
An introduction to Boutiques.
Description:
This tutorial presents the main features of Boutiques to make tools FAIR: finding existing tools, reusing them, and publishing new ones.
Link to notebook icon:
http://boutiques.github.io/images/logo.png
Track:
_tutorials
Why is that resource useful for the neuroscience community:
In recent years the FAIR principles have become a guide for sharing scientific data. Products that implement these principles should be Findable, Accessible, Interoperable, and Reusable. While these guidelines have often been interpreted specifically with data in mind, they can be meaningfully applied to other scientific products such as tools or software. While many database systems and organizational standards are evolving to implement these principles for datasets, the tool landscape remains cloudy.
Platforms such as GitHub allow users to access software, but tools can often be difficult to find and platforms remain mainly suitable for open-source projects. Standards for automatically generated documentation increase the re-usability of software but are unlikely to survive beyond a tool’s supported lifetime. Common frameworks for handling tool arguments provide a consistent interface to tools, though these frameworks vary across libraries. While virtualization engines such as Singularity encapsulate tools and their environments for their re-use, the barrier often remains high to evaluate the performance of a tool within one’s workflow.
Boutiques is an end-to-end approach for producing and consuming FAIR tools:
- Findability is achieved through the public indexing and minting of Digital Object Identifiers (DOIs) for executable tool descriptions.
- Accessibility is facilitated through the accompaniment of tools and both human-readable and executable documentation.
- Interoperability is gained through the adoption of a common and versatile standard for these tool descriptions, Boutiques, and a common application programming interface (API) for medical imaging, CARMIN, which provides a standard set of instructions for accessing data processing services.
- Re-usability is facilitated through integrated testing, simulation, and adoption of these descriptions in high-performance computing (HPC) environments.
How much data storage space do you expect to use:
34MB
How much execution time do you expect users to need:
1 hour.
@glatard Good news! the tutorial nicely fits the scope of NeuroLibre!
We have started working on the integration. I would encourage you to watch the following repository:
https://github.com/neurolibre/boutiques-tutorial
We will be communicating with you on this repository through issues, using your github handle for notifications.
Thanks for submitting your work to NeuroLibre, and looking forward to review & revise the material.
Awesome! I'm now watching that repo, and I assume the 2 current issues aren't for me to fix (although I'd be happy to help, let me know!) and you will assign or @ me if needed.
@glatard the binder link is ready, please check the repository https://github.com/neurolibre/boutiques-tutorial
So does it mean that the tutorial is published in Neurolibre?
You can try the binder link it was working during OHBM.
It can happen that the image need to be pulled again from dockerhub if it is not on the node so the waiting time can be long because of networking issues we have on computecanada.
I tried it and it worked.
Do I have to do anything else for the tutorial to be published?
Dear Tristan, thanks for your patience. I have re opened the issue, as we are just starting the review process. Some people have volunteered to act as test reviewers. I will follow up asap on the boutiques-tutorial repository.
Two reviewers have agreed to provide feedback on this submission. You can follow them at neurolibre/boutiques-tutorial#7 and neurolibre/boutiques-tutorial#6
great news @glatard the tutorial has been approved for merging!!
@ltetrel and I are going to get in touch with the website admin and push the material on neurolibre.conp.ca
We'll be in touch if we run into any unforeseen issue.
Don't hesitate to contact us if you see any issue when the notebook is published.
Congrats on authoring the first ever peer-reviewed neurolibre tutorial, it is a huge milestone for the whole team.
Huge thanks again to @llevitis @martinagvilas and @ltetrel for reviewing this submission.
NB: I will close the issue when the tutorial is published.
Great news, thanks! The process went very smoothly on my side, and reviewers have been very useful indeed!