Reference

Core Terms

RDM (Research Data Management): Planning, organising, storing, documenting, sharing, and preserving research data across the project lifecycle.
DMP (Data Management Plan): A living plan describing how data will be collected, stored, documented, shared, retained, and preserved.
FAIR: Data principles that aim to make data Findable, Accessible, Interoperable, and Reusable.
Metadata: Descriptive information that helps data be understood, located, and reused.
Data lifecycle: The sequence from planning and collection through analysis, sharing, preservation, and reuse.
Personal data: Information relating to an identified or identifiable person.
Special category data: More sensitive personal data, such as health, genetic, biometric, race, religion, or sexual orientation data.
Lawful basis: The legal justification for processing personal data under UK GDPR.
DPIA: Data Protection Impact Assessment used to identify and reduce data protection risks.
Data controller: The organisation that decides why and how personal data are processed.
Processor: A party processing data on behalf of the controller.
Ethics review: Formal review of research involving humans, personal data, or other significant risks.
Retention: How long data are kept.
Preservation: Longer-term keeping of data so they remain usable and understandable.
Version control: A way of tracking changes to files, code, documents, or datasets over time.
Reproducibility: The ability to understand, check, and rerun a workflow or analysis.

Institutional Platforms

Use these descriptions alongside Digital Tools and Research Data Management. Confirm the approved platform for your project’s material before choosing storage.

Platform	Typical use
Microsoft 365	Day-to-day collaboration, communication, writing, and spreadsheet work
OneDrive	Individual working storage with sync and version history
Teams	Group communication, meetings, and shared project working space
SharePoint	Collaborative document storage with permissions and longer-lived team spaces
RIS (Worktribe)	Research lifecycle tracking, outputs, grants, and linked researcher information
UniCore	Finance, HR, procurement, and operational research administration
ORCID	Persistent researcher identifier used to maintain a consistent research identity
TRE Secure Storage	Restricted environment for sensitive or confidential data
CPS Research Storage	Group-managed research storage for backed-up project data

Quick Decision Guide

Where should this work live?

Situation	Usually the best fit
Personal draft notes or early working files	OneDrive
Shared project documents used by a team	Teams / SharePoint
Sensitive or confidential research data	Approved secure storage such as TRE Secure Storage
Proposal, output, or institutional research record	RIS (Worktribe)
Finance or procurement workflow	UniCore
Public researcher identity and linked outputs	ORCID plus institutional systems

Practical Good Practice

File and folder naming

Use names that are consistent, sortable, and readable.

2026-03-23_interview-theme-summary_v01.docx
project-alpha/
  raw-data/
  cleaned-data/
  documentation/
  outputs/

Helpful patterns:

use dates in YYYY-MM-DD format
avoid spaces if files move across systems regularly
use meaningful version labels such as v01, v02, final-reviewed
separate raw, cleaned, and derived data

Minimum documentation to keep

For most projects, try to retain:

who created or updated the file
when it was created or changed
what the file contains
how values, variables, or categories should be interpreted
what processing or transformation has already happened
where the authoritative version is stored

Metadata prompts

If you are not sure what metadata to record, start with:

title
creator or owner
date
format
location
description
methods or source
access restrictions
reuse conditions

Governance and Compliance Reminders

Ethics approval and data protection are related but not identical.
Consent is important, but it is not automatically the lawful basis for research processing.
Personal data can include indirect identifiers, not just names and email addresses.
Sensitive data requires both stronger justification and stronger safeguards.
A good DMP should match your real storage, access, sharing, and retention practices.

Literature and Collaborative Writing

Use alongside Digital Tools.

Reference record: bibliographic details used to identify and cite a source; a PDF attachment is separate.
DOI: a persistent identifier for a research output. Check that it resolves to the item you intend to cite.
Citation trail: earlier references or later citing work connected to a starting paper.
Search log: the question, service, date, exact query, filters and decisions recorded during discovery.
Citation style: the rules used to format citations and a bibliography; formatting does not verify the source.
Tracked changes: proposed edits that an author can review and accept or reject.
Version history: saved states of a document that can support comparison and recovery.

Before citing a source, check its authors, title, year, publication details, identifier and the claim it supports. Label reading notes as abstract-only if you have not read the relevant full text. Agree where the authoritative document and reference library live, who reviews changes and how approval is recorded.

Practice and reusable templates are in Additional Material.

Optional Follow-on: Data Interpretation

These questions connect to Methods for Data Science, which covers data collection, exploration, interpretation and impact in depth.

When reviewing a dataset or chart, ask:

What exactly does each row represent?
Are categories used consistently?
What is missing, ambiguous, or mixed together?
What would a collaborator misunderstand without explanation?
Does the chart help interpretation or distort it?
What action is the audience expected to take from this output?

Optional Follow-on: FAIR and Open Research

FAIR and Open Research Practices develops output assessment, Open Access, preregistration, transparent methods, contribution and responsible sharing. Its reference guide provides practical decision checks. FAIR does not require unrestricted access: a clear, justified retrieval process and useful metadata can support restricted outputs.

Brief AI Writing Check

The writing section of Digital Tools includes a short evidence-checking example: compare a rewrite with its source, preserve qualifications and reject unsupported claims. Check permissions before supplying research material to any external service and record actual AI contributions where applicable.

For prompting, validation and broader research workflows, use the separate Responsible Use of Generative AI course and its reference guide. These are optional follow-on resources.

End-of-Course Checklist

By the end of the course, you should be able to:

choose more appropriate tools for collaboration and storage
explain key governance and RDM concepts in practical terms
design a clearer folder structure and documentation approach
check reference records, citations and source-linked reading notes
agree a clear review process for shared documents
recognise questions that require specialist advice or a follow-on course