Structured Progression
Courses are organised so learners can build skills incrementally and revisit concepts as needed.
About Digital Skills
Learn Skills You Can Use Immediately
The Digital Skills programme is built around practical research workflows, not abstract tool demos. Each module gives learners something they can reuse: a cleaner folder structure, a safer data process, a working automation, a reproducible analysis, or a clearer workflow.
๐งญ Discover
Use our course finder to explore the available courses and modules and identify the training that best fits your research needs.
๐ ๏ธ Build
Sessions mix focused teaching, hands-on tasks, and group discussion so learners can apply ideas straight away.
๐ Reuse
Each course page includes slides, setup notes, examples, and references so learners can return to the materials later.
Open Access
The Digital Skills programme is designed so learners can return to the materials after a workshop and reuse them in their own work. Course pages collect slides, setup notes, practical exercises, example files, and references wherever those materials can be shared openly.
๐ Open by Default
Materials are published on the training site where possible, so learners can revisit them without waiting for a follow-up email or a private file share.
โป๏ธ Built for Reuse
Examples are written as starting points for real workflows, including templates, data checks, automation patterns, and reproducible analysis structures.
๐งญ Clear Boundaries
Where access, licensing, institutional systems, or sensitive data matter, the materials explain what can be reused and what needs local approval or adaptation.
Why This Programme Exists
Modern research projects routinely involve code, large data sets, digital collaboration, and complex computational workflows. Many researchers have limited time to build confidence with software engineering, digital infrastructure, automation, or best practice for managing data.
The programme addresses this gap through a structured training pathway aligned with open science, reproducible research, and research integrity.
Hands-On Learning
Sessions use collaborative exercises, scenario-based tasks, and practical build activities.
Open Science Focus
Modules emphasise FAIR data, reproducible workflows, responsible governance, and transparent documentation.
Discipline-Agnostic Design
The methods and tools apply across research domains, with space for specialist workflows where needed.
Who It Is For
The programme supports a range of experience levels, from learners building digital confidence to those strengthening computational, analytical, and data-driven research workflows.
Doctoral Researchers
Build practical habits for data, automation, analysis, and responsible use of AI.
Early-Career Researchers
Strengthen workflows that can be reused across projects, publications, and collaborations.
Analysts and Professional Services
Improve repeatable digital processes, reporting workflows, and operational data handling.
Training Tiers
Learners can follow the pathway step by step or choose the course that best matches their current project, role, or skills gap.
01
Foundation
Essential Digital Skills introduces the core tools, policies, and principles required for computational research.
02
Extend
Responsible AI and automation courses help learners apply practical guardrails to common research and business processes.
03
Analyse
Python and R courses support reproducible data analysis, modelling, reporting, and debugging.
Delivery Model
Sessions are primarily in person to support discussion, collaboration, and peer learning. Cohorts are cross-disciplinary where possible so learners can compare workflows across different research contexts.
Assessment is formative. Learners receive feedback from instructors, peers, and the practical behaviour of the systems they build.
Materials Usually Include
- Slides
- Learner setup guides
- Practical exercises
- Example data or documents
- Reference pages
- Instructor notes
Contact
For general enquiries, registration, or accessibility queries, contact the Digital Research Service.