newsinfomain ethicallyspeaking 191018

The topic of Big Data has been explored extensively in various sectors and literature. However, there is still a lack of a framework to guide decision-making that involves ethical issues surrounding the use of big data in health and research.

As part of our new series, #EthicallySpeaking, we’ll share articles on the “Ethics Framework for Big Data in Health and Research” developed under the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative by the NUS Yong Loo Lin School of Medicine's Centre for Biomedical Ethics. Watch this space for weekly updates!


  1. Click here for the first article, "Delivering a Practical Framework for Ethical Decision-Making Involving Big Data in Health & Research", which gives an overview of a body of work relating to the consideration of ethical issues involving big data in health and research.

  2. Click here for the second article in the series, "An Ethics Framework for Big Data in Health and Research", which showcases a framework developed by an international working group convened by the SHAPES Initiative.

  3. The third article in the series, "Openness in Big Data and Data Repositories", explores the ethical considerations that arise with the sharing of data through online data repositories in health and biomedical research. It also presents a case study for the application of the ethics framework articulated in previous papers. Click here to read more.

  4. In the fourth installment, we look into the ethical analysis of the use of big data in the field of precision medicine; balancing the benefits of a diverse dataset and the implication of data confidentiality. Click here to read more.

  5. Real-world data is increasingly important in the clinical research landscape for assessing the safety and effectiveness of health-related interventions. In the fifth installment of the series, we explore the issues related to the quality of data collected and how the ethics framework can be used to manage competing and conflicting interests in the generation of real-world data. Click here to read more.