Got a project idea? Submit! Project submissions for Brainhack Donostia 2021 are now OPEN!
In the spirit of Brainhack, participants are encouraged to propose collaborative projects to be developed by small teams “onsite” (through Zoom) during the event. We would like attendees to sign up for projects to promote active collaboration, and hands-on learning, both during and after the event.
Projects can focus on any technique or method, use behavioral or neuroimaging data, or explore any topic related to Open Science. They can involve any stage of the scientific process, from data acquisition to analysis and documentation, as well as data visualization and scientific publication. You’re also encouraged to work on creating tutorials or improving the documentation of a library/program, or even replicate a paper (and dockerize it!). To find out more about the projects carried out last year, check out the Brainhack Global website.
This year, we will ask participants to select a project they’d like to contribute to when they register for Brainhack Donostia. This way, we want to encourage participants to join the event with their mindset ready to collaborate on projects. We hope it will also lead to a better chance of collaborating and networking, and we think it will be helpful for project leaders too. Of course, the selection is not be final, and participants will have the chance to join different projects after the projects are officially presented on the first day.
The activities carried out during Brainhack Donostia 2021 can result in useful output that will be available beyond the current event, as well as new collaborations with peers. We highly encourage you to submit a project for this year’s edition. If you are in need of inspiration, have a look at the project ideas we have received so far.
Projects submitted so far
ICA-AROMA (i.e. ‘ICA-based Automatic Removal Of Motion Artifacts’) concerns a data-driven method to identify and remove motion-related independent components from fMRI data. To that end it exploits a small, but robust set of theoretically motivated features, preventing the need for classifier re-training and therefore providing direct and easy applicability. The aim of this project is to modify the original ICA-AROMA code in order to restructure it into a pure Python package; e.g., dropping FSL calls in favor of pure Python functions.