AI for Science and Government (ASG) Communications Outputs project

This repository is not actively being maintained. Please browse the repo for links to resources, most of which have also been added to the Turing's impact page for ASG

Welcome to the GitHub repository linking communications outputs developed to communicate about the research projects, outcomes and real-world impact from the AI for Science and Government (ASG) research programmes. 🎉

ASG was an integrated research programme at The Alan Turing Institute that aims to demonstrate – via a diverse range of activities organized and presented as an integrated whole – how AI and data science can be used to effectively address significant societal challenges and transform health, science, engineering, and government. The project

The communications outputs listed in this repository were develped with the contributions from the researchers, showcasing their work through a series of white papers and supporting communication outputs about the ASG projects.

Vision

“Demonstrate, showcase and amplify the real-world impact of ASG research.”

Mission

Our goal is to produce a series of communication outputs, including formal reports as well as impact stories, highlighting:

• the research contribution of the ASG programme,
• the unique role ASG plays in cultivating diverse endeavours and
• the influence of the Turing’s network-convening capability ASG's work.

Specifically, the outputs address these questions:

1. What are the ASG research contributions and success stories?
2. Why ASG is uniquely placed to do such integrated research?
3. How does the Turing leverage the convening capability to enhance the quality of ASG work?
4. What is the real-world impact of the ASG research?

Background

ASG was initiated in 2018 with a £38.8M investment from the UK government's strategic priorities fund. ASG supported a range of projects in different domain areas including applications within the Criminal Justice System, Health, Data Science for Science, Engineering, and Urban Analytics. In addition to these themes, ASG researchers also worked across three large cross-theme projects that focus on:

• Shocks and Resilience: improving policy-making by linking healthcare and economic data and models, preparing for future shocks and making government more resilient
• Ecosystems of Digital Twins: combining methodology from Engineering and Urban Analytics to develop integrated systems of digital twins
• Environment and Sustainability: linking heterogeneous environmental data and models to help assess the widespread impact of climate change, from arctic sea ice loss to food security

The project was wrapped up in March 2023.

Outputs from this project

In this repo, we are providing links to a series of communication outputs. These address key issues for researchers, policy-makers and other stakeholders on the future of AI in science and government. All documents are being co-created and shared openly for further development and reuse.

One of the primary outcomes is a series of White Papers, which offers an opportunity for ASG as a programme to reflect on:

• learnings using evidence from ASG research,
• key challenges that have emerged,
• recommendations for the future and
• possible calls to action for the wider research community, government, policymakers and the public.

White Papers

• Core Theme - Environment and Sustainability: Tackling climate change with data science and AI. Cite as: Conner, A., Hosking, S., Lloyd, J., Rao, A., Shaddick, G., & Sharan, M. (2023). Tackling climate change with data science and AI. Zenodo. doi: 10.5281/zenodo.7712969
• Core Theme - Ecosystem of Digital Twine: Towards ecosystems of connected digital twins to address global challenges. Cite as: Bennett, H., Birkin, M., Ding, J., Duncan, A., & Engin, Z. (2023). Towards ecosystems of connected digital twins to address global challenges. Zenodo. doi: 10.5281/zenodo.7840266
• Core Theme - Shocks and Resilience: Building resilience in government using data science, Cite as: Bennett, H., Dorobantu, C., MacArthur, B., & Margetts, H. (2023). Building resilience in government using data science. Zenodo. doi: 10.5281/zenodo.7973613

Supporting Materials

• A browsable report on AI in Environment and Sustainability with examples from ASG: AI in Environment and Sustainability Exemplars

Briefing Notes

A series of Briefing Notes were published that are shared in this repository. Please browse them in the directory for ASG Briefing Notes

The Coffee Pod: Podcast

The Coffee Pod: a short series about the Turing's AI for Science and Government fund, that gives you the chance to hear about the people behind the project. Conceived by Achintya Rao, hosted by Bea Costa Gomez and produced by Luca Lane.

• Episode 1: Ruoyun Hui
• Episode 2: Domenic DiFrancesco, who worked on Digital Twins for Fleets and Supply Chain Management in the Ecosystems of Digital Twins cross-theme.
• Episode 3: Tom Andersson, who worked on the Turing/British Antarctic Survey on Environmental models: Bridging the spatial scales, from surface sensors to satellite sensors in the Environment and Sustainability cross-theme project
• Episode 4: Fernando Benitez, who worked on Spatial Modelling in the Shocks and the Resilience cross-theme project.
• Episode 5: Malvika Sharan, who worked on The Turing Way in the Tools, Practices and Systems theme.
• Episode 6: Hussein Rappel, who worked on Digital Twins in Infrastructure and Construction project in the Complex Systems Engineering theme.

Impact Stories

• Better together: The people-centred approaches driving forward data ethics
• Open minds: How open-source tools are broadening the horizons for data science
• Twinning ways: how digital twins will change our world for the better

Previous impact stories are listed online: https://www.turing.ac.uk/about-us/impact/asg.

Leads and Contributors

• The ASG programme is under the leadership of:
  • Professor Ben MacArthur, Director of AI for Science and Government, Deputy Programme Director for Health and Medical Sciences, and Turing Fellow
  • Professor Jonathan Rowe, AI for Science and Government Programme Chair, Programme Director for Data Science for Science, and Turing Fellow
• The Programme Management team:
  • Hannah Sweeney, Programme Manager
  • Zaynab Ismail, previous Research Project Manager
• The The Tools, Practices and Systems (TPS)
  • Research Applications Management (RAM) Team
  • Aida Mehonic, Senior Researcher - Research Applications and RAM team lead
  • Jennifer Ding, RAM
  • Alden Connect, RAM
  • Research Community Management (RCM) Team
  • Malvika Sharan, Senior Researcher - Open Research and RCM Team lead
  • Achintya Rao, previous ASG Community Manager
  • Zeynep Engin, AI for Science and Government Theme Lead for TPS
• The Turing Communications team members:
  • James Lloyd, Scientific Writer
  • Hayley Bennett,contracted freelancer
• The Turing Events team is involved throughout the project for ASG events-related support

ASG overview

Partners and projects

• The Tools, Practices and Systems (TPS) programme at the Turing represents a cross-cutting set of initiatives that seek to build open source infrastructure that is accessible to all and to empower a global, decentralised network of people who connect data with domain experts.
  • The ASG Community Manager was funded by the ASG and sits in the Reseach Community Management Team hosted under the TPS programme.
  • The Research Application Management group led by Aida Mehonic was piloted under the ASG programme. Alden Conner and Jennifer Ding were involved in the production of one whitepaper each while supporting multiple ASG projects as Research Application Managers.

Policies

All the Turing Institute's policies and Code of Conduct apply to the activities in this project carried out by the Turing researchers and employees. For both internal and external contributors, we recommend familiarising yourself with the following policies and guidelines from the Turing Institute that we apply to this project:

• Equality, Diversity and Inclusion framework
• Events code of conduct

Licence

Content in this repository, copyright of The Alan Turing Institute, is licensed under the MIT licence (code) and Creative Commons Attribution 4.0 International licence (documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use, and with no additional restrictions.

Contact

This project is currently being maintained by Malvika Sharan, TPS Senior Researcher - Open Research and Research Community Manangement. Please email msharan@turing.ac.uk to contact her.