As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcends borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders, with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale, and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science so as to promote international collaboration, through federated constructs such as the International Brain Initiative (IBI).

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Drawing on more than eight years working to implement Responsible Research and Innovation (RRI) in the Human Brain Project, a large EU-funded research project that brings together neuroscience, computing, social sciences, and the humanities, and one of the largest investments in RRI in one project, this article offers insights on RRI and explores its possible future. We focus on the question of how RRI can have long-lasting impact and persist beyond the time horizon of funded projects. For this purpose, we suggest the concept of ‘responsibility by design’ which is intended to encapsulate the idea of embedding RRI in research and innovation in a way that makes it part of the fabric of the resulting outcomes, in our case, a distributed European Research Infrastructure.

Recent advances in Artificial Intelligence (AI) have led to intense debates about benefits and concerns associated with this powerful technology. These concerns and debates have similarities with developments in other emerging technologies characterized by prominent impacts and uncertainties. Against this background, this paper asks, What can AI governance, policy and ethics learn from other emerging technologies to address concerns and ensure that AI develops in a socially beneficial way? From recent literature on governance, policy and ethics of emerging technologies, six lessons are derived focusing on inclusive governance with balanced and transparent involvement of government, civil society and private sector; diverse roles of the state including mitigating risks, enabling public participation and mediating diverse interests; objectives of technology development prioritizing societal benefits; international collaboration supported by science diplomacy, as well as learning from computing ethics and Responsible Innovation.

For a number of years, facial features removal techniques such as ‘defacing’, ‘skull stripping’ and ‘face masking/blurring’, were considered adequate privacy preserving tools to openly share brain images. Scientifically, these measures were already a compromise between data protection requirements and research impact of such data. Now, recent advances in machine learning and deep learning that indicate an increased possibility of re-identifiability from defaced neuroimages, have increased the tension between open science and data protection requirements. Researchers are left pondering how best to comply with the different jurisdictional requirements of anonymization, pseudonymisation or de-identification without compromising the scientific utility of neuroimages even further. In this paper, we present perspectives intended to clarify the meaning and scope of these concepts and highlight the privacy limitations of available pseudonymisation and de-identification techniques. We also discuss possible technical and organizational measures and safeguards that can facilitate sharing of pseudonymised neuroimages without causing further reductions to the utility of the data.