In LEAPS-Innov WP7, we are working on novel compression schemes for data recorded at synchrotron light sources. In the process, we discovered a lot of genuine ways to reconstruct and record data. Unfortunately, many of these workflows were never shared or archived in a way that has reproducibility in mind.
With this co-working sprint, we would like to change that! The goal of this co-working sprint is to pair scientists/engineers with people experienced in using a workflow engine that fosters reproducibility. They then work together (remotely) over the course of 6 weeks to facilitate an automated workflow (e.g. that is fit for HPC execution). The final goal is to publish this workflow on platforms like WorkflowHub so that the community can reuse these workflows.
This event marks the kick-off of our co-working sprint.
We hope to attract beamline scientists, coders and (data) engineers that have never heard about workflow engines. If do however have experience with workflow engines, you can also apply. We will give priority to teams working on data from synchrotron light sources.
We guess that your team has a workflow in a coding language of choice available - for example as a sequence of python/shell scripts (potentially for use on a HPC cluster) or as a single jupyter notebook. For participating, you need to be able to share your data (or at least share example data) and code in a FAIR fashion.
If you can tick all yes to the above, fill out the call for abstracts until Jan 31, 2023, and block February 10, 2023. That is all you have to do. We will get in touch with you shortly after your submission.
We are happy to have the following workflow mentors at our disposal:
Should you be interested to support our event, please get in touch with the organizers. The more mentors we have, the more teams we can host.
We are also open with respect to tooling and will not be limited to one particular workflow engine. The workshop is organized to advocate open tools that foster reproducible open science. The engines we will offer mentoring in should empower every scientist to reuse and reproduce workflows from other institutes or centers irrespective of hardware platform and operating system.
The organisation of this workshop is supported by