Open Data Day at Cambridge Brainhack – Open Knowledge International Blog

"Brainhack Global 2017 consisted of 40 satellite events around the world on March 2nd, 3rd and 4th 2017. Brainhack is a unique hackathon and unconference that brings together researchers with disparate backgrounds to collaborate on open science projects in neuroimaging.

We had more than 40 participants at the Cambridge satellite event of Brainhack Global representing early career researchers from multiple university departments and research institutes. Over three days we supported each other as we learned new skills and developed analyses to investigate neuroimaging data.

The organising committee worked hard to foster a warm and friendly atmosphere. We know how hard it is to go outside their comfort zone and we wanted to make sure that everyone felt welcome. We had a strict code of conduct and made it very clear that everyone was welcome, no matter your race, gender, level of coding ability, or choice of programming language.

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There are, however, major challenges associated with sharing human brain imaging data:

• It may be possible to identify individual people from their data. It can be very difficult to anonymise some data sets and sensitive information such as their history of mental health difficulties or intelligence measures should be protected. It can be very hard to know how to best navigate the ethics of sharing data and there may even be different requirements in different countries.
• Some human neuroimaging datasets are large (a few hundred gigabytes per acquisition) and therefore many existing repositories are not suitable. Although members of Brainhack Global were working on the Brain Imaging Data Structure project it is not yet widely adopted. This means it is difficult to organise data in a way that it is interpretable to other researchers.
• Not only are the datasets large, but as it is very expensive to collect brain imaging data, there are many stakeholders and collaborators. It is unlikely that an early career researcher will be able to make the decision to share the data from their study.
• There is a steep learning curve associated with learning new skills, software or platforms. Adding an additional open data burden on PhD students and postdocs may require a lot of dedicated time that they simply do not have."