MRC Biomedical Data Science Leadership Awards Brochure

This brochure has been developed collaboratively by the five MRC Biomedical Data Science Leadership Award Projects and is intended to disseminate our shared vision for this award as well as our own project activities. Introduction to award and projects: The MRC Biomedical Data Science Leadership Awards (BDSLA) aim to strengthen the inclusion, quality, and recognition of data science within biomedical research by fostering thought leadership, innovative approaches, and a more inclusive research culture. MRC BDSLA was launched in response to the MRC’s 2022 Biomedical Data Science Review, which identified the need for coordinated action across four key challenges: developing strong biomedical data science leadership; improving access, recognition, and support for multidisciplinary teams; enhancing quality, standards, and professionalisation; and adopting inclusive approaches to growing skills capacity and improving career pathways. In 2024, the MRC funded five cross-institute collaborative awards to address leadership and collaboration challenges and to generate evidence on how biomedical research can better incorporate, resource, and recognise high-quality data science across the research landscape. These projects bring together interdisciplinary teams to develop practical approaches, tools, and frameworks that can be adopted across the biomedical research community: ABDC – Advancing Biomedical Data Science Careers BIOMEDASA – Biomedical Data Science Accelerator HxC – Healthier Science through Collaboration INTEGRATE – Embedding a supportive culture for data science in biomedical research PROMOTE – Progressing Routes and Opportunities through Mentoring, Openness, Training, and Equity Alongside their individual objectives for supporting interdisciplinary research, the five teams have developed strong cross-award relationships, supported by an innovative MRC mechanism that provided additional resources for collaborative activities. This multi-institute and cross-sector approach has enabled knowledge exchange across a wide range of UK research organisations regarding both barriers and new ways of working to support interdisciplinary research.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-16
DOI
https://doi.org/10.5281/zenodo.19605439
Primary Topic
Interdisciplinary Research and Collaboration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MRC Biomedical Data Science Leadership Awards Brochure

Giulia Tomba, Denise Bianco, Rachel Etherington, Emma Karoune et al.
Zenodo (CERN European Organization for Nuclear Research)
Interdisciplinary Research and Collaboration
article

MRC Biomedical Data Science Leadership Awards Brochure

Giulia Tomba, Denise Bianco, Rachel Etherington, Emma Karoune, Eva Caamaño Gutiérrez, Daria Sokolova, Eirini Koutsouroupa, Jessica Shilton, Christina McDonald
article en

Abstract

This brochure has been developed collaboratively by the five MRC Biomedical Data Science Leadership Award Projects and is intended to disseminate our shared vision for this award as well as our own project activities. Introduction to award and projects: The MRC Biomedical Data Science Leadership Awards (BDSLA) aim to strengthen the inclusion, quality, and recognition of data science within biomedical research by fostering thought leadership, innovative approaches, and a more inclusive research culture. MRC BDSLA was launched in response to the MRC’s 2022 Biomedical Data Science Review, which identified the need for coordinated action across four key challenges: developing strong biomedical data science leadership; improving access, recognition, and support for multidisciplinary teams; enhancing quality, standards, and professionalisation; and adopting inclusive approaches to growing skills capacity and improving career pathways. In 2024, the MRC funded five cross-institute collaborative awards to address leadership and collaboration challenges and to generate evidence on how biomedical research can better incorporate, resource, and recognise high-quality data science across the research landscape. These projects bring together interdisciplinary teams to develop practical approaches, tools, and frameworks that can be adopted across the biomedical research community: ABDC – Advancing Biomedical Data Science Careers BIOMEDASA – Biomedical Data Science Accelerator HxC – Healthier Science through Collaboration INTEGRATE – Embedding a supportive culture for data science in biomedical research PROMOTE – Progressing Routes and Opportunities through Mentoring, Openness, Training, and Equity Alongside their individual objectives for supporting interdisciplinary research, the five teams have developed strong cross-award relationships, supported by an innovative MRC mechanism that provided additional resources for collaborative activities. This multi-institute and cross-sector approach has enabled knowledge exchange across a wide range of UK research organisations regarding both barriers and new ways of working to support interdisciplinary research.

Zenodo (CERN European Organization for Nuclear Research)
European Bioinformatics Institute (GB), University of Leeds (GB), University of Liverpool (GB), University of Dundee (GB), University of Oxford (GB), UK Dementia Research Institute (GB), The Alan Turing Institute (GB), Health Data Research UK (GB), University of York (GB), Newcastle University (GB), University of the Arts London (GB), Teesside University (GB), University of Sheffield (GB), University of Edinburgh (GB)
Openalex Percentile: Top 9%
Interdisciplinary Research and Collaboration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.