Sustainability through success stories

WHEN: 8.00 (GMT-4) July 25 (Tuesday)

During this symposium, the Open Science SIG will emphasize the theme of “sustainable open science” via insights shared by 4 speakers with extensive expertise within the open science community. During the talks, open science goals, such as: increased transparency, accountability, equity and collaboration in knowledge production, will be addressed in a non-didactic manner. The contribution of Open Science to moving the neuroimaging field forward and important practical sharing of experience towards a successful, sustainable Open Science research culture will also be discussed.

The target audience are postgraduate students, early researchers, people new to open science as well as those with an interest in practicing open science. The audience will gain insights on the fundamentals of open science and the means of furthering their success in neuroimaging science by embracing the success stories shared by our panelists.

The recorded talks will be released in our YouTube and DouYu channels after the conference!

Hackathons and early career development

Speaker: Hao-Ting Wang

The hackathon format has been part of the neuroimaging community for a decade. The events include Brainhack, OHBM hackathon, and various code sprints from software-based projects. The increased popularity corresponds with the needs of programming in research, inspired by large datasets and a focus on reproducible research. Most importantly, the environment fosters informal collaboration and networking for researchers of all career stages. I will walk through my journey with Brainhack, research projects that stemmed from hackathons, all the friends I made along the way, and how researchers can benefit from this community.

The value and challenges of open data sharing for translational research.

Speaker: Richard Bethlehem

Drawing from recent large scale collaborations, I will discuss the progress and remaining obstacles for open and transparent sharing of neuroimaging data. Specifically, I will talk about the necessity of large scale representative data for creating community benchmarks that can be used in translational research. The challenges that I aim to briefly discuss mainly pertain to acknowledgement and valuation of open contributions, restrictions related to consent or sharing practices and technical challenges related to harmonization and high performance computing.

Computational resources and other opportunities for open science practices

Speaker: Subapriya Suppiah

With the increasing availability of brain imaging data, there is a growing need for more robust computational resources that can provide an architecture for standardized brain imaging data to be shared, accessible and be analyzed across institutions. Brain research infrastructures such as EBRAINS in Europe and ARDC Nectar Research Cloud in Australia avail the shared resources provided by a consortium of institutions and facilitate cross-institutional research collaborations. During my talk, I will share the benefits of gaining access to computational resources in neuroimaging, which will increase your opportunities for improving your skills and ambition towards cultivating an open science approach in your research practices.

Embracing open code practices

Speaker: Theodore D. Satterthwaite

It is widely acknowledged that open code is a prerequisite for transparency, reproducibility, and progress in computational neuroscience. Despite this, adoption of open code remains uneven in the field. Here I will summarize our center’s experience with creating, maintaining, and ultimately learning to love open code - with an emphasis on common obstacles for investigators and outstanding challenges for the field.

Learning Objectives:

  1. Getting inspired by the success stories to overcome challenges of open science at different academic levels (i.e., early career vs. PI)
  2. Gaining the appreciation of how open science practice is impacting different aspects of neuroscience
  3. Appreciation regarding various challenges associated with the open science practices and insight into strategies that help overcome these and ensure reproducible, sustainable open science of high quality in the long term.