@@ -25,7 +25,9 @@ The Open Analysis Working Group has worked with researchers to capture details o
International community data standards - specifically the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io) - have been employed to ensure that shared pipelines are compatible with tools developed elsewhere. This working group is also committed to actively engaging with the future development of BIDS, to ensure lifetime compatibility between FSL and the wide data standards.
### Education
Another large focus of this working group is developing programming literacy among WIN members, to support users in creating robust code to run their analysis. Significant efforts have been made to update the [FSL training material](https://fsl.fmrib.ox.ac.uk/fslcourse/), which is now available for free both internally and externally, and has been updated to include basic training in Unix command line access. The FSL course is being run remotely and at reduced registration rates for the first time in 2020, for improved accessibility and inclusivity.
Another large focus of this working group is developing programming literacy among WIN members, to support users in creating robust code to run their analysis. Significant efforts have been made to update the [FSL training material](https://fsl.fmrib.ox.ac.uk/fslcourse/), which is now available for free both internally and externally, and has been updated to include basic training in Unix command line access. The FSL course was run remotely and at reduced registration rates for the first time in 2020, for improved accessibility and inclusivity.
We run a series of programming training workshop as part of our [MRI Graduate Programme](https://www.win.ox.ac.uk/training/mri-graduate-programme) which is open to all WIN members at any career stage. This includes our intensive "python for MRI" [pytreat](https://git.fmrib.ox.ac.uk/fsl/win-pytreat) material, which has been developed and refined by our FSL developers and users since 2018.
### FSL - the FMRIB Software Library
FSL 6.1.0 (due to be released in the second half of 2022) will be based on the [`conda`](https://docs.conda.io/en/latest/) environment and package manager. Conda makes installing FSL, and reproducing a FSL installation straightforward. Conda also allows you to install only the tools you need, instead of a full FSL installation, which is useful for containerising your analysis pipeline. More details can be found [https://git.fmrib.ox.ac.uk/fsl/conda/docs/](here).
