Winners announced for UK DRI’s first 'Prize for Computational Reproducibility in Dementia Research'

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We are delighted to announce that PhD students Kitty Murphy and Brian Schilder, both from UK DRI at Imperial, have been awarded the Institute’s first computational reproducibility prizes. The scheme is designed to promote the development and open release of sustainable and reproducible code as part of dementia research output.

Scientific computation plays a key role in biomedical research. Yet reproducing a computational study’s results can, sometimes, be surprisingly difficult. Ideally, the code underlying a study should be well documented, independently verifiable, and suitable for other researchers to build upon to avoid duplication of efforts and greatly accelerate our understanding of neurodegeneration.

The UK DRI strives to become an institute renowned for generating and sharing high quality, FAIR (findable, accessible, interoperable and reusable) research output with the scientific community. As part of the efforts, we have established a new award scheme - Prize for Computational Reproducibility in Dementia Research. The prize aims to highlight some of the excellent efforts already taking place at the UK DRI and encourage all to adopt practices that will make computational studies robust, reproducible and shareable.

Reproducibility is important for scientific progression, and I'm so grateful to be part of the UK DRI where this is encouraged. PhD Student Kitty Murphy,

This year’s entries were evaluated by members of the UK DRI Software Working Group who considered criteria such as quality of sustainability planning and ease of reproducibility. The two winning entries are from PhD students based in the labs of Group Leader Dr Nathan Skene and Emerging Leader Dr Sarah Marzi:

Kitty Murphy (UK DRI at Imperial)

CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders

The judges were impressed with the clear presented, well-explained, user-friendly R package with easy-to-follow vignette and preprint.

Brian Schilder (UK DRI at Imperial)

echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline

The judges thought the entry represented a substantial, highly reproducible piece of work, which included, for example, a clearly laid out and well documented code and clear explanation of re-deployment requirements and data used in the experiments.

Kitty Brian

On receiving the award, PhD Student Kitty Murphy, said:

"I am thrilled to have won the computational reproducibility prize alongside my fantastic team member Brian Schilder. Reproducibility is important for scientific progression, and I'm so grateful to be part of the UK DRI where this is encouraged."

PhD Student Brian Schilder, added:

“Investing in reproducible research practices has not only made my work better, but has opened up the possibility that it may be useful to the global scientific community for years to come."

You can find the two winning entries on the UK DRI GitHub repository.

Article published: 09 December 2021