Essential Digital Skills

Foundation pathway for confident, defensible digital research practice

Course At A Glance

  • Level: Foundation
  • Best for: Early-career researchers, research staff, and professional services colleagues
  • Main themes: Collaboration, delivery, governance, research data management, and defensible digital research practice
  • Format: Core lesson sequence for practical digital research work
  • Outcome: Better day-to-day judgement about where work should live, how it should be documented, and how research decisions can be made more safely and transparently

Why This Course Matters

Modern research is full of small digital decisions that carry real consequences. Where should project files live? What counts as good enough documentation? When does a governance issue need escalation? How can a collaborator check your sources and decisions?

Essential Digital Skills is designed to make those decisions easier. It gives learners a practical foundation for working inside the university research ecosystem with more confidence, less guesswork, and stronger habits for collaboration, compliance, and evidence.

What You Will Actually Do

This is not a passive overview course. Learners work with realistic scenarios, map real workflows, critique weak practices, and improve them.

Across the course you will:

  • map research tasks to the right institutional systems
  • find literature, check reference records and review writing with co-authors
  • think through delivery and coordination problems
  • identify governance and data protection risks
  • improve data management and folder structures
  • improve practical decision-making across common research workflows

Who This Is For

This course is especially useful if you:

  • are starting to work more seriously with digital research workflows
  • want to feel more confident using university platforms and support systems
  • need stronger habits for documentation, storage, sharing, and team coordination
  • regularly make decisions about data, evidence, or reporting but have never had formal training in these areas

It is also a strong entry point for researchers who plan to continue into Methods for Data Science.

You Will Build Confidence In

Working Better Together

Use the right tools for shared work, understand how systems connect, and reduce friction in collaboration.

Managing Risk

Recognise when data, ethics, policy, or security issues need a more careful response.

Making Data More Usable

Improve structure, documentation, metadata, and reuse potential rather than treating data management as an afterthought.

Planning Defensible Workflows

Connect tools, roles, systems, policies, and data decisions into workflows that are easier to explain and maintain.

Learning Journey

Lesson What you will tackle Why it matters
01 Digital Tools Institutional systems, researcher identity, digital security, literature discovery, reference management, and collaborative writing Helps you find and organise evidence, choose tools, and review research writing with collaborators
02 Digital Delivery Planning, coordination, sprint thinking, and shared ownership Gives teams a more structured way to move work forward
03 Data Governance and Policy GDPR, ethics, classification, and compliance Strengthens judgement around lawful, ethical, and institutionally defensible practice
04 Research Data Management DMPs, storage, metadata, archiving, and FAIR reuse Turns RDM from paperwork into practical workflow design

Follow-on Courses

For assessing output usability and choosing transparent, responsible research practices, continue to FAIR and Open Research Practices. Its two lessons build on Research Data Management through FAIR assessments, publication decisions and an Open Research strategy.

For help applying a skill to a particular digital task, explore Digital Skills Clinics.

For data quality, exploration, visualisation, and impact, continue to Methods for Data Science.

For dedicated AI teaching, see the separate Responsible Use of Generative AI course. It offers modules on research opportunities and risks, prompting and validation, and applied workflows. This is optional further learning; it is not an additional Essential Digital Skills session.

What Learners Typically Leave With

By the end of the pathway, learners should be more able to:

  • choose better tools and storage locations for their work
  • identify weak points in project coordination and documentation
  • ask sharper questions about data sensitivity, sharing, and governance
  • improve folder structures, naming, and metadata for collaboration
  • plan more defensible next steps in their own digital research practice

How To Prepare

You do not need programming experience for this course. It helps to arrive with:

  • access to your university account and core platforms such as M365, Teams, OneDrive, and SharePoint
  • a device suitable for group exercises and practical tasks
  • one current project or workflow in mind that you can use as a live example

Detailed preparation guidance is available in Setup.

Course Materials

Learners

  • Setup: what to bring, what to access, and how to prepare
  • Additional Material: follow-up practice, reusable search/review templates and source guidance
  • Reference: glossary, platform guide, and practical checklists
  • Discussion: prompts for breakout work and end-of-session reflection

Instructors

Community and Course Information