The Royal Society’s Disruptive Technology for Research project conducted by the Science Policy team aims to understand the landscape of data-driven and artificial intelligence-based (AI) technologies across different fields of scientific research. The project will further articulate the impact and risks data-driven technologies can have, outline cases of success and seeks to understand the factors that have slowed adoption and how this might be improved. The project will look at different scientific fields as case studies to offer recommendations on how the UK government (amongst other organisations) can best support the development, adoption, and uses of such technologies.
It will seek to highlight:
- Which fields have been uniquely transformed by data driven technologies
- How digital technologies are contributing to novel scientific research methods
- How data-driven technologies are reshaping the process of scientific research and impact reproducibility
- How the UK government (and others) can best support the development, adoption and uses of such technologies
- How universities will need to respond to these changes, in particular when it comes to the training of technicians and scientific infrastructure needs
- The role and of interdisciplinary work and how to best support it
- The risks and limitations of the use of some of these technologies for scientific research.
As part of this project the Royal Society is commissioning a taxonomy of AI-related technologies and their current applications to scientific research. The findings of this research in terms of a visual representation of the taxonomy as well as a written summary of key insights and trends will be presented in the Disruptive Technology for Research report. The inclusion of this research will enable end users to have an overview of the different types of artificial intelligence related technologies used in different fields of scientific research. This will therefore add context to the challenges, opportunities and the Royal Society’s recommendations presented through the reports chosen case studies.
To submit a quote please review the RFQ briefing document (PDF) and provide the requested information by 19 January 2023.