Creating Ethical Guidelines for Animal Data Governance in Neuroscience

Published on August 29, 2023

Just like human data, animal data generated in scientific research needs proper governance mechanisms to regulate its sharing and reuse. While human data is subject to increasing regulation, there are no established frameworks for governing non-human animal data. This article discusses the need for animal data governance from the perspective of neuroscience. It highlights the ethical concerns surrounding the use of animals in research and emphasizes the importance of considering different ethical views when establishing governance mechanisms. The study conducted semi-structured interviews with 12 participants to gather insights on this topic. The participants suggested that factors such as variations in regulations, ethical principles, values, beliefs, and data quality necessitate the implementation of animal data governance. The article concludes that a procedural approach, which acknowledges different ethical positions and fosters cross-cultural collaboration, is essential for effective animal data governance. To delve deeper into this intriguing topic and explore potential solutions, check out the full article!

IntroductionScientific research relies mainly on multimodal, multidimensional big data generated from both animal and human organisms as well as technical data. However, unlike human data that is increasingly regulated at national, regional and international levels, regulatory frameworks that can govern the sharing and reuse of non-human animal data are yet to be established. Whereas the legal and ethical principles that shape animal data generation in many countries and regions differ, the generated data are shared beyond boundaries without any governance mechanism. This paper, through perspectives from neuroscience, shows conceptually and empirically that there is a need for animal data governance that is informed by ethical concerns. There is a plurality of ethical views on the use of animals in scientific research that data governance mechanisms need to consider.MethodsSemi-structured interviews were used for data collection. Overall, 13 interviews with 12 participants (10 males and 2 females) were conducted. The interviews were transcribed and stored in NviVo 12 where they were thematically analyzed.ResultsThe participants shared the view that it is time to consider animal data governance due to factors such as differences in regulations, differences in ethical principles, values and beliefs and data quality concerns. They also provided insights on possible approaches to governance.DiscussionWe therefore conclude that a procedural approach to data governance is needed: an approach that does not prescribe a particular ethical position but allows for a quick understanding of ethical concerns and debate about how different positions differ to facilitate cross-cultural and international collaboration.

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