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AI for Multiple Long Term Conditions: Research Support Facility

Welcome to Getting Started repository for the AI for Multiple Long Term Conditions Research Support Facility! 🎉 Here you'll find guides and resources to help you get started with the AIM RSF projects, information on how to contribute and use the repositories, and other resources generated to support the 7 AIM consortia that form our research community.

🗺️ Background

The Research Support Facility (RSF), based at The Alan Turing Institute with collaborators from Swansea University and the University of Edinburgh, offers AI and advanced data science support to the research teams part of the NIHR's Artificial Intelligence for Multiple Long-Term Conditions (AIM) project, and acts as a hub to foster collaboration across the AIM consortia.

The AIM project's research combines data science and AI methods with expertise from health, care and social science with the goal of identifying new clusters of disease and understand how multiple long-term conditions (MLTC) develop over the life course.

As a Research Support Facility, we will be covering multiple research and community activities to work towards that goal, as well as building strong connections and collaborations across the AIM consortium aligned with 5 Core Themes.

Theme 1: Reproducible, secure and interoperable infrastructure

This theme will bring different research collaborations across the UK together within a trusted research environment to facilitate data, software and analysis sharing. This will support a progressive move from reproducible, to reproduced, to reused research artefacts and outputs, maximizing the return on the NIHR’s investment in the overall research programme.

Theme 2: Accessible, research ready data

Data wranglers, experts in data curation and quality control, will work with the Research Collaborations to align datasets and standards. This will enable a broad range of data to be incorporated into extended analyses within AIM and beyond.

Theme 3: Community building and training

This theme aims to build connections between early career researchers across the AIM programme so their existing expertise can be shared across AIM and into the wider network. The other core aspect of the theme will be training and mentorship in the digital skills researchers need to deliver open source outputs from their Research Collaborations.

Theme 4: Patient and public involvement and engagement

Enhancing existing patient and public involvement networks across the AIM programme, this theme will support and empower people with lived experience of MLTC to co-create the research with the teams. Online engagement activities such as talks and seminars that are accessible to all will set the standard for patient involvement in other healthcare areas in the future.

Theme 5: Sustainability and legacy

This theme will work with researchers to embed outputs in existing communities, both clinical and academic, and engage with policy makers to coordinate a long term investment in MLTC research. It is a key part of ensuring that AIM research continues beyond the end of this specific investment and the impact of the work conducted benefits as many people as possible.

📣 Communication Channels

  • We have a monthly Open invitation knowledge share series which is kicking off in March 2022. You can find out more and register for the events here.
  • You can join our Slack Workspace to connect with the team and the wider community.
  • We hold monthly lunch sessions for the Early Career Researchers, and anyone involved in Patient Recruitment, Involvement, or Community Engagement. Reach out to Evelina Gabasova egabasova@turing.ac.uk, or Sophia Batchelor sbatchelor@turing.ac.uk to get involved.
  • The Turing website has info on the RSF, who's involved, and other info about the current projects.

☎️ Contact

This repository is maintained by the Community Manager for the AIM RSF, Sophia Batchelor. You can reach her at sbatchelor@turing.ac.uk

♻️ License

License: CC BY 4.0

The work in this repository is licensed under the Creative Commons Attribution 4.0 International license for documentation and made public to our collaborators within the AIM consortium and the larger research community. 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. Please note that this license does not extend to cover other materials or repositories in this organization unless directly specified.

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