The vision for 2023
The Global Health Data Science Hub is a free, open access knowledge exchange hub for anyone working in data science, data management, data sharing, and data access within health research. The aim of the hub is to provide resources, signpost to training opportunities and to build a community for data scientists and others working in Global Health to find and share information and connect and collaborate. Specifically, the Hub aims to:
- Provide open access to tools and toolkits, resources across the data re-use project cycle, including protocols, templates, generic policies, webinars, events and training opportunities
- Promote trustworthy data access, re-use and analysis through FAIR data principles (including data infrastructure and governance) and connecting excellence in areas where evidence is lacking
- Support a global health data science community of practice to share resources, interact, connect and collaborate.
Priority topics for 2023
In 2023, the hub will give special attention to three priority areas in the field of health data science:
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Getting Started with R for Health Data Science: Recognising the growing demand for R programming skills in the healthcare domain, the community hub will provide resources and tutorials specifically tailored to beginners in health data science. We will focus on foundational R concepts, data manipulation and analysis techniques, data visualisation, and specialized packages and techniques relevant to health research.
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AI and ML Techniques to Support Health Research: The integration of artificial intelligence and machine learning techniques in health research holds immense potential for advancing medical knowledge and improving patient outcomes. The hub content will explore AI and ML methods applied to healthcare data, including electronic health records, medical imaging, surveilance, genomics, and clinical trials.
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Federated Approaches to Data Sharing and Access: As the need for collaboration and data sharing across healthcare organisations and research institutions grows, federated approaches to data sharing and access are gaining prominence. The hub content will delve into the concepts, methodologies, and technologies behind federated analysis, learning, privacy-preserving techniques, and secure data aggregation. There will also be a focus on regulatory and ethical considerations associated with sharing sensitive health data.
By focusing on these three priority areas, the community hub will equip aspiring health data scientists and researchers with the necessary tools and knowledge to navigate the intersection of R programming, AI/ML techniques, and data sharing in the healthcare domain. We aim to provide accessible, practical, and up-to-date resources to empower individuals and contribute to advancements in health research and data science.