Data Science in Cancer Research Award

Are you an Imperial based cancer researcher with data sets that you think would benefit from the expertise of a mathematician, computer scientist or someone who works with data? OR

Are you an Imperial based mathematician, computer scientist, or someone who has a methodology that you would like to apply to the cancer field and would like to find cancer research collaborators / data sets?

(currently closed to applications)

The CRUK Imperial Centre, together with NIHR Imperial Biomedical Research Centre have now launched our call for proposals for Data Science in Cancer Research Awards. This scheme aims to strengthen and promote collaborations between maths, computing or data scientists with cancer researchers (clinical or non-clinical), funding projects to analyse, exploit or address challenges associated with using cancer data sets.

Applications will be considered in any area of cancer research, provided you canarticulate the problem you are looking to address and how the data will be used. Projects must be looking to apply data science approaches to existing data sets. Applications must consist of at least one lead applicant with an established background in cancer research (clinical or non-clinical) and one lead applicant from a data science field. Post-Doctoral level researchers are also eligible to apply, provided they have the support/approval of their line manager.

Awards can provide funding of up to £25k of funding, for up to 12 months. Funding can be used for salary ( e.g. buy out existing post-doc time or for a new post), access to data, research consumables directly attributable to the work e.g. access to high performance computing or research equipment (up to a value of £5k). Please note that this award will only cover direcly incurred costs in line with CRUK guidlines.

Useful data resources:

Imperial's Clinical Analytics, Research and Evaluation (iCARE) project

This system provides the ability to manage access and control to specific de-identified data resources allowing full audit of every action that staff take when using Imperial College Healthcare NHS Trust (ICHNT) data, with audit data accessible in real-time to the ICHNT Data Protection Office.

Big data and analytical unit (BDAU)

The Big Data and Analytical Unit is a multi discipline team which collaborates with a large network of researchers across the college to ensure the maximum use, impact and dissemination of research using healthcare data.

Clinical Practice Research Datalink (CPRD)

Clinical Practice Research Datalink (CPRD) is a real-world research service supporting retrospective and prospective public health and clinical studies. CPRD is jointly sponsored by the Medicines and Healthcare products Regulatory Agency and the National Institute for Health Research (NIHR), as part of the Department of Health and Social Care.

Funding: Up to £25k for a maximum of 12 months

Application deadline: 19 October 2020

Research Summaries

Prior to this call, researchers interested in this funding opportunity but who were seeking collaborators were invited to submit a description of their datasets, research interests/problems and data science methodologies. The Centre will contact those who submitted summaries individually to suggest potential collaborations.

You can find the individual summaries for interested parties in the downloadable documents below, split into cancer and data scientists, which may further aid in identifying potential collaborators.

Please note, you do not need to have submitted an EOI to be eligible to apply.

The summaries have been split into 'Cancer Research Summaries' - which contains an overview of problems that cancer researchers think would benifit from the expertise of someone working with data and 'Data Scientist Summaries' which gives an overview of of mathematicians, computer scientists and others working with data methodologies that could be applied to cancer research.

To discuss a project, eligibility or costings, please contact the Director of Operations, Chloe Stockford.

Data Science call - Data Scientist summaries‌‌