Advancing Biomedical Data Science Careers: Equity, Diversity, and Inclusion Strategy

Advancing Biomedical Data Science Careers Equity, Diversity, and Inclusion Strategy 1. Introduction The MRC-funded Advancing Biomedical Data Science Careers (ABDC) project is a collaboration between two world leaders in biomedical data science – The Alan Turing Institute and EMBL’s European Bioinformatics Institute (EMBL-EBI). It began in February 2025 and is set to conclude in January 2027. From our extensive experience in this area, and working with a diverse range of existing and new partners, our project outputs will enable organisations to incorporate data science skills into their teams and establish greater understanding of the common language needed for skills and careers in this domain. For this project, we are adopting a broad definition of biomedical research, as defined by the Medical Research Council (MRC): ranging from omics to microscopy to medical imaging to population cohort to environmental data. To achieve its objective, ABDC is committed to fostering a diverse, equitable, and inclusive environment where open and collaborative research thrives. This document presents a series of considerations and actions initiated within the project, and strives towards making equity, diversity and inclusion (EDI) an integral part of ABDC. It outlines our strategic approach to embedding EDI principles into the project’s activities, operational modes, research process, and community engagement. This strategy aligns with The Alan Turing Institute’s and EMBLEBI’s broader EDI objectives and aims to address the specific challenges and opportunities within the ABDC project. 2. ABDC’s vision for EDI ABDC’s vision is to create a diverse, inclusive, and equitable environment that fosters a strong culture of open and collaborative research. This vision is grounded in the understanding that solving complex challenges in biomedical research requires the contributions of a broad and collaborative community. As set out in the last MRC strategic review (2022–2025), there is an urgent need in biomedical research for a shared framework of data science careers, to enable skills mobility and recognition of biomedical data science roles and team approaches across different contexts. It is therefore crucial that all voices contribute to the project’s success. 3. Strategic Goals for EDI in ABDC The Alan Turing Institute’s EDI strategy is structured around seven key objectives which focus on its role as a national body, research institute, employer, and other EDI commitments, including inclusive practices in governance and central functions. EMBL’s EDI strategy, which applies to the whole organisation including EBI, is structured around eight objectives, which builds on EMBL’s values and focus on people, internal processes and policies, research culture and leadership, and external engagement. This 2-year ABDC EDI strategy is designed to align with these objectives and supports the project’s mission by emphasising equity, diversity, and inclusion as central to identifying priority areas for developing skills, knowledge, and behaviours across the biomedical data science ecosystem – and to building inclusive, diverse career pathways The ABDC project has identified these strategic goals: 1. ABDC as a community • Equal Voice Ensure community members have opportunities to contribute from the outset, with their voices heard, represented, and reflected in the project’s activities and outputs. • Inclusive Leadership Empower community members to take on leadership roles and ensure their perspectives are reflected in decision-making while encouraging existing leaders to embed inclusive practices in their leadership. 2. ABDC as a research project • Embedding EDI Principles into the ABDC research process Integrate EDI considerations – aligned to Turing and EMBL-EBI EDI strategies – into all aspects of ABDC research activities, ensuring a virtuous feedback loop with the community. • Representing different voices Establish an Advisory Board composed of data professionals from a range of backgrounds and biomedical domains to ensure that different voices are not only heard but actively represented and meaningfully involved throughout the process. 3. ABDC in the wider ecosystem • Democratisation of knowledge Promote the sharing of knowledge related to careers in biomedical data science and create opportunities for a broader variety of people, skills, and roles – ultimately contributing to the formation of truly diverse teams. • Inclusive and Supportive Community Foster a welcoming environment where all individuals feel valued. Support a diverse population of practitioners and promote variety of career pathways. • Measuring and Reporting on EDI Progress Establish clear metrics and reporting mechanisms to track progress toward EDI objectives. 4. Opportunities for Advancing EDI in Biomedical Data Science The ABDC project and the biomedical data science field present unique opportunities to enhance EDI: • Diverse Roles and Entry Routes: The field is characterised by a wide range of roles at various levels, offering multiple entry routes that can help attract a broader and more inclusive intake. This is also reflected in the ABDC project, which is led by an interdisciplinary team and engages with diverse partners across the ecosystem. • National Outreach and Inclusion: The project is part of the broader Biomedical Data Science Leadership (BDSL) Awards, funded by the Medical Research Council. This initiative brings together a consortium of research organisations across the UK (The Alan Turing Institute, EMBL-EBI, University of Liverpool, University of Edinburgh, HDR UK, UK DRI, University of Oxford, Edinburgh Cancer Research, University of Dundee-MRC PPU, University of Leeds, University of Sheffield, University of York, Teesside University, and Newcastle University) along with their extended networks of academic, research, government, and industry partners. Together, we are working to identify key areas for action within the biomedical data science field, with the aim of building shared understandings and expanding impact at the national level. Combined with the extensive experience of the Turing and EMBL-EBI in this field, these efforts position ABDC as a catalyst for developing a shared language across diverse skill sets and for fostering inclusive, diversified career pathways within the domain. • Building on Existing Strengths: The inherent diversity of disciplines within the biomedical data science field, such as medical microbiology, clinical epidemiology, biomedical engineering, computer science, and software engineering, just to mention a few, provides a solid foundation for enhancing EDI. 5. Challenges in the Biomedical Data Science field Data and data science are transforming the world, and expertise in data science is in extremely high demand. As highlighted in the MRC’s recent strategic review (2022), there is an urgent need in biomedical research for a shared framework for data science careers. Such a framework, currently lacking, is essential for enabling skills mobility, recognising biomedical data science roles, supporting diverse career pathways, and facilitating team-based approaches across varied contexts. The ABDC project takes a qualitative, people-centred approach, focusing on the roles that are included in this space. It documents these roles across different career stages by drawing on a diverse sample of individuals from across the field. In addition to mapping roles, organisations, and skill sets, the project will identify existing EDI challenges and offer practical recommendations to address them. 6. Community Engagement and Inclusivity To contribute to an inclusive community for Biomedical Data Science professionals, the following strategies will be implemented: • Community Engagement: Actively engage with members of the growing Biomedical Data Science community to document and showcase examples of successful implementations of diverse data science roles and teams across different types and scales of organisations. We have established a diverse Advisory Board to ensure regular feedback and open discussion. • Widening Participation: Inspire professionals across the Biomedical Data Science ecosystem through targeted outreach and engagement activities (including conferences, collaboration with other BDSL projects, co-creation workshops, and the collection of case studies) open to individuals at all career stages and in a wide variety of roles. • Accountability: Conduct regular evaluations of inclusivity efforts with the Advisory Board and make adjustments based on feedback. Emma Karoune (PI) and Denise Bianco (Senior Research Community Manager) will be responsible for conducting the evaluations, ensuring that EDI principles are implemented in practice. 7. Laying the Foundations for Long-term Success This 2-year ABDC EDI plan will focus on establishing strong foundational practices: • Understanding the Landscape: Facilitate the recognition and understanding of different roles, career pathways and skill sets across the landscape with EDI considerations to be embedded in all operational aspects, including research activities and community engagement. • Training and Development: Map a selected set of existing competency frameworks and training resources from a wide range of providers targeting various professional groups, ensuring our outputs address diverse access points to biomedical data science. We will also identify training needs across these groups to help develop a diverse workforce for the field. • Team science approaches: Support organisations in embedding interdisciplinary, collaborative approaches in biomedical data science through the promotion of cross-sector best practices. Ongoing communication with various stakeholders will support continuous improvement, especially in EDI and inclusive career development. 8. Objectives and Action Plan The following objectives have been defined for the 0–24 month period, along with associated actions: 1. ABDC as a community Objective: Support a diverse and inclusive Biomedical Data Science community. Actions: • Establish a regular series of meetings with the community to offer participants the chance to provide input. This includes Advisory Board meetings, collaborative co-creation workshops, and stakeholder working group meetings. • Measure current views on inclusivity in the project and gather suggestions for improvement through case studies and ongoing community engagement. • Regularly collect diversity data and feedback on inclusivity through the Diversity Monitoring Form and Equality Impact Assessments (EIAs). • Establish EDI reporting and define relevant key performance indicators (KPIs). • Reduce duplication of effort by sharing best practices openly and reproducibly for use and adaptation. 2. ABDC as a research project Objective: Embed EDI principles into all research activities. Actions: • Ensure that all research is assessed to ethical standards and evaluated regularly, adhering to Turing’s Research Ethics Process (TREx) • Develop a robust process for gathering relevant EDI data across different project activities and events. • Ensure that research and knowledge remain “as open as possible, as closed as necessary” including research data, processes, and outputs. • Build equality impact assessments into operational processes for all relevant activities. • Ensure consistent engagement and feedback processes with the Advisory Board. 3. ABDC in the wider ecosystem Objective: Understand the EDI landscape in Biomedical Data Science. Actions: • Map the landscape of organisations, initiatives, and activities in the Biomedical Data Science space, keeping EDI principles at the core. • Engage the community through feedback and discussions on EDI - via workshops, participation in relevant national and international events, and input from the Advisory Board – to inform landscape mapping and prioritisation. • Identify and agree on priority EDI areas for ABDC to focus on during training needs analysis and case study development, to inform the final report. • Promote knowledge exchange and share best practices across MRC Biomedical Data Science Leadership Award projects through regular meetings, resource sharing, and collaboration via an EDI working group. 4. Measuring and Reporting on EDI Progress Objective: Implement mechanisms to measure and evaluate progress in EDI. Actions: • Regularly assess community views on diversity and inclusion. • Champion EDI principles across project outputs. • Establish EDI reporting processes and define KPIs. The team has identified processes and mechanisms to ensure regular evaluation of EDI practices: • Equality Impact Assessments • Diversity Monitoring data • Regular reporting after events and community activities • Advisory Board meetings every 6 months • Turing’s Research Ethics (TREx) process for ABDC research work 9. Sharing Best Practices / Lessons learned To effectively share lessons learned from ABDC’s implementation of its EDI strategy, we will establish a communication and collaboration approach that engages: • Internal stakeholders: the Research Community Managers and Skills teams at the Turing, the Training team at EMBL-EBI, and the EDI teams at both organisations. • External stakeholders: the ABDC Advisory Board, MRC BDSL Award projects, individuals in roles related to community management, and EDI champions. This may include cross-departmental workshops where successful EDI initiatives are highlighted, encouraging the exchange of ideas and strategies. Internal platforms for knowledge sharing (e.g., Internal blogs and Lunch and Learn series). We will also insert a section on the ABDC’s webpage that signposts ABDC’s EDI strategy. EDI documentation will be stored in ABDC’s GitHub repository and Zenodo, where resources, case studies, and guidelines can be easily accessed and shared. By creating these opportunities for open discussions and resource sharing, we aim to ensure that EDI practices are firmly embedded in Biomedical Data Science, as a community but also discipline. 9.1 Replicating project’s approach Some lessons that could be shared and replicated across the Turing and EMBL-EBI include: • Embedding openness into regular workflows, shared resources, and templates. • Building EDI into project reporting and operational planning. • Clearly articulating current EDI activities to support the EDI teams (e.g., equality impact assessments, diversity monitoring, open sourcing – making outputs and documentation available to all). • Reinforcing the message that EDI is a collective responsibility. • Adopting a qualitative, people-centred approach to investigating and documenting team science practices. • Prioritising realistically, recognising challenges and proposing actionable outputs. • Focusing on interdisciplinary collaboration and problem-solving, not just technical skills. 10. Conclusion This document provides an overview of the ABDC project’s EDI strategy, highlighting the importance of a structured approach to addressing the challenges and opportunities within the biomedical data science field. This project will empower cross-domain working so that collaborative team science approaches lead the future of biomedical research. The outlined objectives and action plans aim to foster a more inclusive and diverse environment that reflects the values of The Alan Turing Institute, EMBL-EBI, the ABDC project core team and partners, and the broader biomedical data science community. The ABDC project is committed to making EDI a cornerstone of its activities, embedding it into its operational practices and fostering a diverse, inclusive culture throughout the project’s duration and beyond, empowering members of the community to contribute and thrive. 11. Funding acknowledgment This project is funded by the MRC Biomedical Data Science Leadership Awards (grant number MR/ Z50662X/1). We would also like to acknowledge the in-kind contributions of The Alan Turing Institute and EMBL-EBI.