Promote broad collaborative activity, networking and open science

Lead Research Organisation: University of Cambridge


The major output for this would be the development of an outward facing repository of BSU resources that can be used by the wider scientific community utilising, where appropriate, existing technology such as Gitlab and Zenodo. By unifying our approach as a Unit, and thus increasing our resource discoverability, we can share our approach with others.

Technical Summary

The current CoVID-19 pandemic has highlighted the need to improve discoverability of research outputs of much of the scientific community. Much of the expertise, software code and useful tools to which timely access would have been beneficial were often difficult to identify or source. Significant and time-consuming administration in accessing resources has also been a barrier to the speedy response necessary at this time.

It is a clearly held consensus across the scientific community that access to useful resources, including analytical tools, software code or expertise should be simplified and that lessons learnt from this current situation should inform best practice going forward. Whilst CoVID-19 may have highlighted the need for this, the problems already existed and were acknowledged in BSU’s last QQR document. The challenge for us all is to find workable solutions to sharing tools and expertise responsibly and with appropriate guidance, without creating such enormous extra administration and over-heads that it becomes impossible.

We propose to appoint a Knowledge Facilitator within MRC Biostatistics Unit (MRC BSU) who would have the following roles:
1. Conduct a scoping exercise across MRC Units and Institutes and other organisations such as HDRUK, Wellcome and the Alan Turing Institute to understand and learn from their approach to sharing research outputs and reproducibility and to forge links to develop a workable best practice.
2. Develop and disseminate central online resources for the outputs of MRC BSU research and training, such as code, analytical tools and training courses following the FAIR principles of good research practice.
3. Build, following the example of, and working with, the established University of Cambridge Data Champion network, a network committed to developing best practice regarding research output dissemination within other MRC Units and the wider MRC funding and research community.


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