Researchers & academics
Lab · Researchers
Full curriculum document
Researchers and academics running rigorous, publishable work
Contents · 6 units
- 1Data: provenance & ethicsLAB-U1 · Phase 1 · 70 GLH · Internal portfolio
- 2Method & computeLAB-U2 · Phase 2 · 90 GLH · Internal portfolio
- 3Experimentation & analysisLAB-U3 · Phase 3 · 90 GLH · Internal portfolio
- 4Peer review & publicationLAB-U4 · Phase 4 · 80 GLH · Portfolio + exam
- 5Reproducibility & open releaseLAB-U5 · Phase 5 · 60 GLH · Internal portfolio
- 6Collaboration & translationLAB-U6 · Phase 6 · 60 GLH · Internal portfolio
Overview & approach
A result that cannot be reproduced is not yet knowledge. Lab is organised around the evidence chain — data, method, compute and review — so every claim is traceable from raw data to published finding.
The first-principles learning loop
- 1Problem
- 2Understanding
- 3Reasoning
- 4Design
- 5Implementation
- 6Testing
- 7Reflection
- 8Improvement
Every unit runs this loop, then goes round again — deeper each time.
Competencies & responsible practice
Core competencies
- Communication & collaboration
- Critical thinking & problem solving
- Creativity & imagination
- Citizenship
- Digital literacy
- Learning to learn
- Self-efficacy
Ethics & responsible practice
- Digital citizenship & online safety
- Data protection & privacy
- AI ethics, bias & fairness
- Environmental sustainability (compute cost)
- Financial literacy & entrepreneurship
- Inclusion & accessibility
- Mental well-being & healthy screen habits
- Career & life skills
The 6 units in full
LAB-U1 · Phase 1 · 70 GLH · Internal portfolio
Data: provenance & ethics
Strand: Research data foundations
Key inquiry: “Where did this data come from, who does it represent, and what may we ethically do with it?”
You ship: A versioned, documented, citation-ready dataset with a datasheet and licence.
LO1 · Curation
- • Source, clean and version a dataset with clear provenance
- • Write a datasheet documenting collection and limits
LO2 · Ethics & representation
- • Assess representation, consent and privacy risk
- • Apply an appropriate licence and access policy
Core competencies
- Critical thinking & problem solving
- Digital literacy
- Citizenship
Ethics & responsible practice
- Data protection & privacy
- AI ethics, bias & fairness
Hands-on practice
- Curate an African/global dataset and document its provenance.
- Audit it for representation and privacy risk.
- Publish a datasheet others can cite.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Versions a dataset with support. |
|---|---|
| Proficient | Documents provenance and ethics independently. |
| Expert | Publishes a citation-ready, ethically sound dataset. |
LAB-U2 · Phase 2 · 90 GLH · Internal portfolio
Method & compute
Strand: Experimental method
Before this unit: A documented dataset (Unit 1).
Key inquiry: “How do we design experiments and use scarce compute so the answer is trustworthy and affordable?”
You ship: A reproducible experiment pipeline with baselines, seeds and a compute budget.
LO1 · Design
- • Design controlled experiments with baselines and seeds
- • Schedule GPU compute against a cost/energy budget
LO2 · Reproducibility
- • Version code, configs and environments end to end
- • Report uncertainty and avoid evaluation pitfalls
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Learning to learn
Ethics & responsible practice
- Environmental sustainability (compute cost)
- Data protection & privacy
Hands-on practice
- Reproduce a baseline on shared GPU before extending it.
- Track compute cost and energy against a fixed budget.
- Containerise the pipeline for one-command reproduction.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Runs experiments with support. |
|---|---|
| Proficient | Designs reproducible, budgeted experiments independently. |
| Expert | Delivers a pipeline peers reproduce exactly. |
LAB-U3 · Phase 3 · 90 GLH · Internal portfolio
Experimentation & analysis
Strand: Experimentation & statistical analysis
Before this unit: A reproducible pipeline (Unit 2).
Key inquiry: “How do we turn runs into trustworthy findings rather than cherry-picked numbers?”
You ship: An experiment suite with baselines, ablations and honest statistical analysis.
LO1 · Experiments
- • Run controlled experiments with baselines and ablations
- • Isolate what actually drives the results
LO2 · Analysis
- • Report significance, confidence intervals and effect sizes
- • Test robustness across seeds and data splits
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Digital literacy
Ethics & responsible practice
- Environmental sustainability (compute cost)
- AI ethics, bias & fairness
Hands-on practice
- Run ablations and report confidence intervals, not just means.
- Test robustness across seeds and data splits.
- Kill a result that does not survive analysis.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Runs experiments with support. |
|---|---|
| Proficient | Analyses significance and robustness independently. |
| Expert | Produces findings a reviewer would trust. |
LAB-U4 · Phase 4 · 80 GLH · Portfolio + exam
Peer review & publication
Strand: Scholarly communication
Before this unit: Analysed, trustworthy results (Unit 3).
Key inquiry: “How does a finding survive expert scrutiny and become part of the record?”
You ship: A peer-reviewed paper and reproducible artefact submitted to a venue.
LO1 · Writing
- • Write a clear, honest paper with limitations stated
- • Prepare a reproducible artefact for release
LO2 · Review
- • Participate in structured peer review, giving and receiving
- • Revise rigorously against reviewer feedback
Core competencies
- Communication & collaboration
- Critical thinking & problem solving
- Learning to learn
Ethics & responsible practice
- AI ethics, bias & fairness
- Career & life skills
Hands-on practice
- Draft and internally review the paper against venue criteria.
- Release code/data with a reproducibility checklist.
- Submit and track the review process.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Drafts and submits with support. |
|---|---|
| Proficient | Writes, reviews and revises to standard independently. |
| Expert | Produces a submission-ready, reproducible contribution. |
LAB-U5 · Phase 5 · 60 GLH · Internal portfolio
Reproducibility & open release
Strand: Open, reproducible science
Before this unit: A submitted or reviewed paper (Unit 4).
Key inquiry: “What must we release so anyone can reproduce and build on this work?”
You ship: A fully reproducible artefact — pinned code, data and environment — released under an open licence.
LO1 · Reproducible release
- • Pin code, data and environment for one-command reproduction
- • Write a reproducibility checklist and a clear README
LO2 · Open licensing
- • Choose an appropriate licence and archive with a DOI
- • Document responsible-use limits for the artefact
Core competencies
- Digital literacy
- Citizenship
- Communication & collaboration
Ethics & responsible practice
- Data protection & privacy
- Inclusion & accessibility
Hands-on practice
- Containerise the project for one-command reproduction.
- Complete a reproducibility checklist end to end.
- Archive the artefact with a citable DOI.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Releases an artefact with support. |
|---|---|
| Proficient | Ships a reproducible, licensed release independently. |
| Expert | Publishes an artefact peers reproduce and cite. |
LAB-U6 · Phase 6 · 60 GLH · Internal portfolio
Collaboration & translation
Strand: Open science & impact
Before this unit: A reproducible, released finding (Unit 5).
Key inquiry: “How does research reach other researchers, partners and the public — and change what they do?”
You ship: An open, collaborative research output plus a partner-facing translation of the findings.
LO1 · Open collaboration
- • Collaborate in a shared research space with partners
- • Maintain an open, well-documented project others can join
LO2 · Translation
- • Translate findings into practice for a partner
- • Communicate impact to a non-expert audience
Core competencies
- Communication & collaboration
- Citizenship
- Self-efficacy
Ethics & responsible practice
- Financial literacy & entrepreneurship
- Inclusion & accessibility
Hands-on practice
- Onboard a collaborator into your open project.
- Write a partner brief on what to change in practice.
- Give a public talk that a non-expert can follow.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Shares work with support. |
|---|---|
| Proficient | Runs an open, collaborative project independently. |
| Expert | Drives real adoption of findings by partners or the field. |
What you walk away with
A portfolio of 6 real, shipped artefacts — one per unit, assessed to standard.
- 1 A versioned, documented, citation-ready dataset with a datasheet and licence.
- 2 A reproducible experiment pipeline with baselines, seeds and a compute budget.
- 3 An experiment suite with baselines, ablations and honest statistical analysis.
- 4 A peer-reviewed paper and reproducible artefact submitted to a venue.
- 5 A fully reproducible artefact — pinned code, data and environment — released under an open licence.
- 6 An open, collaborative research output plus a partner-facing translation of the findings.
Proficiency rubric (Emerging → Expert)
| Proficiency level | What the evidence shows |
|---|---|
| Emerging | Completes the work with guidance; the artefact functions for the core case and the practitioner can explain what it does and why. |
| Proficient | Works independently, handles real-world edge cases, evaluates the result, and justifies design decisions from first principles. |
| Expert | Delivers work adopted by real users or partners, reasons explicitly about trade-offs — cost, risk, safety — and extends beyond the brief with original contribution. |