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. Author manuscript; available in PMC: 2023 Feb 16.
Published in final edited form as: Neuron. 2021 Dec 15;110(4):600–612. doi: 10.1016/j.neuron.2021.11.017

Table 2. Needs to consider for International Data Governance (IDG) across the Neuroscience data lifecycle.

Several issues are at stake when data must be considered from an international perspective. Some of these issues are general to any project involving human and animal data. Yet, some specific needs and challenges must be considered when crossing international borders.

Data Lifecycle Stages Neuroscience Data Governance Needs that affect international sharing
Collection Informed consent. How to collect consent for international sharing and utilization of data
Sampling bias. How to represent a very heterogeneous population across ethnic groups and cultures
Regulatory differences and legal basis for data collection. How to understand the different laws for animal welfare or data protection
Processing Anonymization, de-identification, and pseudonymization. How to assure that subjects’ privacy can be kept while retaining scientific utility of the data
Regulatory differences and legal basis for data processing. How to understand the data protection laws in different countries
Curation Standardization. How to understand the different standards for metadata schemes across countries
Data-curation transfer agreements. How to establish agreements that allow data curation outside of the owner’s nation when needed
Security. How to assure that risks of data breaches are minimized
Archiving and preservation

  1. Retention policy. How long shall the data be preserved

  2. Data controllership, stewardship, or custodianship. Who owns rights on or controls data?

  3. Funding. Who is paying for data archiving? Should access be free for all users?

  4. Security. How to ensure risks of data leaking are minimized over time as technology changes
Application and utilization Incidental findings. How to communicate findings that pertain to the health of the study participant
Minimization. How to ensure studies use the minimal amount of data so as to minimize risks to the participants (e.g., re-identification or privacy break-ins)
Misuse. How to ensure data are not misused or misapplied for ethically, legally or socially unacceptable purposes.
Biases in analysis and results interpretation. How to mitigate data analysis bias concerns or misinterpretation of results.
Dual use: How data can be used responsibly for both civil and military application.
Commercial exploitation: What restrictions are available regarding using data for economic gain?
Sharing Access control. How to manage access to data, authorization and data use agreements (DUA) across investigators, institutions and countries.
Third party and international sharing. How to overcome regulatory limitations to sharing data to assure effective scientific impact in international projects.
Risks of re-identification. How to prevent potential risks of re-identification given advancements in machine learning and AI.
Licensing. How to approach data licensing and intellectual property concerns when required.
Attribution. How to cite and keep track of contribution to data collection, processing or curation.
Deletion Inappropriate retention. How to ensure data is retained and deleted responsibly after it has been used.
Loss of data and unintended deletion. How to ensure resilience to human mistakes.

This table lists some of the most critical aspects that must be considered when embarking on international projects for brain research.