Bringing it together

Research Skills — Week 10

Recap

Recap

• Report structure
• Summary swap
• Peer review in science
• Common pitfalls
• Telling the story honestly
• Peer review briefing
• Wrap-up

The full journey

You’ve designed an investigation, collected and analysed data, checked assumptions, fitted models, and identified limitations.

Now: write it up so someone else can understand it and act on it.

w01-ppdac-ring.R

• [11:05–11:10] This is the last content delivery. It should leave students confident about what a good report looks like.
• Point at the ring: where this week sits in PPDAC (Week 1, “The cycle”).

Questions?

Submit questions:

PollEv.com/geol

text geol to 07480 781235

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

Report structure

• Recap

Report structure

🎓 Concept block 1

• Summary swap
• Peer review in science
• Common pitfalls
• Telling the story honestly
• Peer review briefing
• Wrap-up

The policy report

Section	Length	Purpose
Summary	200–300 words	The finding, the evidence, the recommendation
Introduction	200–300 words	The question, why it matters
Methods	250–400 words	Data, approach, why these tests
Results	500–750 words	Figures, tests, plain-language interpretation
Discussion	~500 words	What it means, caveats, recommendation

• [11:10–11:35] Walk through each section using the Phase 1 faithful exemplar as a concrete example. Leave ~8 minutes at the end for the figure pair and the gallery.

The summary test (revisited)

A reader who stops after the summary should still know:

1. What you investigated
2. What you found
3. What you recommend

If any of these are missing, the summary needs work.

Methods: enough to reproduce

Your methods section should answer:

• Where did the data come from?
• What did you do to clean/prepare it?
• Which tests, and why?
• What software and versions?

Could someone clone your repo and replicate your analysis?

Results: figures that tell a story

Every figure should have:

• A clear “so what”
• A caption that tells the reader what to see
• Axis labels with units
• A reference in the text

Every test result should include:

• The test used
• The p-value and an effect size or CI
• A plain-language interpretation

A figure that earns its place

w10-labelled-figure.R

• Same data as Pitfall 2 later in this deck. Point at each element in turn — axis names, units, the “so what” in the subtitle, the source — so students recognize the stripped version when it reappears.
• The subtitle percentages are computed by the script, not typed. Say so: a number written by hand into a caption is a number that can drift out of step with the figure above it.

Every line equally loud

w10-annotation-before.R · idea after Heiss (2026)

• This is Week 4’s “after” standard: axes, units, title, source, legend ordered by size. Nothing here is wrong. Ask: what is this figure about? Expect several answers — that is the problem.
• Watch the room shuttle between legend and lines. Then ask which line is bioenergy: gas and bioenergy are two near-identical dark colours. Six series is more than a legend can carry.
• Numbers for narration, from the script (shares of total generation, 2025): gas 31.7%, wind 29.4%, bioenergy 14.0%, nuclear 12.2%, solar 6.8%, coal 0.0%.

One story, labelled directly

w10-annotation-after.R · idea after Heiss (2026)

• Same data, same scales. Three decisions, and each is one you can make in your report:
  1. Label the lines directly. The legend is gone; the eye never leaves the data.
  2. Highlight the story, grey the rest. The grey lines stay: they answer “compared to what?” without competing.
  3. Annotate the point that matters. 2017 is the first year bioenergy generated more than coal (9.4% vs 6.7%; in 2016 coal was still just ahead, 9.1% vs 8.9%). The script finds the year; nobody typed it.
• The title now states the finding. Carbon Brief does exactly this — it is on the next slide.
• Tie back to Phase 1: this is the chart the biomass briefing was about. Much of that bioenergy is imported wood pellets burned at Drax, a former coal station — which is why “carbon-neutral?” was a real question.
• The labels are placed without an add-on package: the script nudges them apart just far enough not to overlap. Students can open it.
• For more on labels: Cara Thompson, Level up your labels (useR! 2022).

Good figures in the wild

Each makes one decision well. Look at the figure, then ask what it chose not to show.

• Carbon Brief — UK’s electricity was cleanest ever in 2024: every title states the finding; the fuel chart labels its lines and greys out nuclear.
• Our World in Data — Share of electricity by source: sources and downloadable data sit beneath the chart, and the reader can add a country — compared to what? built in.
• Financial Times — Visual Vocabulary: choose the chart by the relationship you want to show, not by habit.
• Ed Hawkins — Show your stripes: drops axes and numbers entirely for one unmissable message. Right for a poster; would it survive a policy report?
• The Pudding — Women’s pockets are inferior: measures real jeans, draws the pockets to scale, and asks what actually fits.

• [3 min, within 11:10–11:35] Don’t open them all in the room — pick one (Carbon Brief is closest to their reports) and leave the rest as homework before they finalize figures.
• The stripes are a deliberate contrast: a brilliant public-engagement graphic that would lose marks in a report, because a reader cannot check a single number. Decisions depend on the audience.
• The deck has preview-links: auto, so a click opens the page inside the slide. If a site refuses to be framed and the preview is blank, ctrl/cmd-click to open it in a new tab.

Summary swap

• Recap
• Report structure

Summary swap

✏️💬 Exercise 1

• Peer review in science
• Common pitfalls
• Telling the story honestly
• Peer review briefing
• Wrap-up

Trade summaries

Swap your draft summary with someone from a different group.

As a reader, answer:

1. What is the main finding?
2. Do I trust it?
3. What would I do with this information?

If the reader can’t answer these from the summary alone, it needs work.

• [11:35–11:50] Students write (if they haven’t) or refine, then swap and discuss.

Attendance

Show the QR from https://attendance.durham.ac.uk/mytimetable, then ← back to the task.

Peer review in science

• Recap
• Report structure
• Summary swap

Peer review in science

🎓 Concept block 2

• Common pitfalls
• Telling the story honestly
• Peer review briefing
• Wrap-up

How it works

Submit → Editor assigns reviewers → Anonymous critique → Revise and resubmit.

• [11:50–12:05] Peer review as self-correction mechanism. Keep the three list slides brisk: students met peer review in Week 4.

Why it matters

• It’s how science self-corrects
• Catches errors the authors missed
• Challenges assumptions
• Improves clarity

Its weaknesses

• Slow
• Biased toward established researchers and ideas
• Doesn’t catch fraud well
• Can be superficial

What makes a good review

• Specific — not “this could be better” but “the CI is missing from Table 2”
• Constructive — what to fix, not just what’s wrong
• Focused on argument and evidence — not just grammar

Your Week 4 GitHub Issues review was peer review. You’ll do it again now, with higher standards.

Common pitfalls

• Recap
• Report structure
• Summary swap
• Peer review in science

Common pitfalls

💬✏️ Exercise 2: HolmesCo’s greatest hits

• Telling the story honestly
• Peer review briefing
• Wrap-up

Quick-fire: spot the problem

Six excerpts from fictional reports. Two minutes each. What went wrong, and at which PPDAC stage?

• [12:05–12:20] These are cumulative — each pitfall connects to a specific week’s content. Students should recognize most of them.
• The stage is revealed with each problem. Plan: 5. Analysis: 3. Conclusion: 1, 2, 4, 6. Most go wrong at Conclusion, the “communication gap”: the analysis may be fine, the report isn’t.

Pitfall 1

“The data clearly demonstrate that offshore wind is the most cost-effective energy source.”

Conclusion. Not supported by the data shown. The report only compared three sources and used levelized cost from 2019.

Pitfall 2

w10-unlabelled-figure.R

Conclusion. The reader can’t interpret it. A figure that isn’t discussed is decoration, not evidence.

Pitfall 3

“p = 0.04, therefore the effect is significant and the policy should be adopted.”

Analysis. No effect size, no CI, no discussion of practical significance. “Is that a big number?”

Pitfall 4

“The results prove that solar power will eliminate fossil fuel use by 2040.”

Conclusion. Overstatement. “Prove” is almost never appropriate. Extrapolation without caveats.

Pitfall 5

“We used statistical analysis to test the hypothesis.”

Plan. Methods too vague to reproduce. Which test? Why? On what data?

Pitfall 6

A report with no limitations section.

Conclusion. Every analysis has limitations. Omitting them doesn’t make them disappear — it makes the reader distrust you.

Telling the story honestly

• Recap
• Report structure
• Summary swap
• Peer review in science
• Common pitfalls

Telling the story honestly

🎓 Concept block 3

• Peer review briefing
• Wrap-up

The key habits

These are the questions that run through the whole course:

• [12:20–12:40] Tie the whole arc together. Each refrain came from a specific week.

The refrains

“Is that a big number?” / “Compared to what?”

Week 2

“What assumptions are we making?”

Week 3

“How plausible was this before we tested?”

Week 6

“What is the model not capturing?”

Week 8

Shape the message, not the data

A figure for a reader is built around one message:

• Write the headline first: what should the reader take away?
• Cut or aggregate detail that doesn’t advance the story; mute axes and gridlines.
• Connect the numbers to your readers’ interests and lives.

Choosing what to emphasize is editing. Changing the evidence to fit the story is HolmesCo.

• [3 min, within 12:20–12:40] The pull-idea from Krzywinski & Cairo, “Storytelling”, Nature Methods 10, 687 (2013), https://doi.org/10.1038/nmeth.2571. Its standfirst: “Relate your data to the world around them using the age-old custom of telling a story.” Their test for the message is the newspaper headline your study would get; then shape the figure (aggregate, emphasize, sequence the panels) so the headline is what the reader sees. Paraphrase; don’t read it out.
• Two more points of theirs worth saying aloud: leaving readers to draw their own conclusions is risky, and “always be accurate”. A story that bends the evidence is the traitor briefing.
• Link back to the coal/bioenergy pair: greying four lines was editing. Deleting gas and wind, so that bioenergy looked like the thing that replaced coal, would have been advocacy: wind grew far more. That is the Week 4 traitor line, and the difference between analysis and advocacy this block is about.
• Suggested reading before submission: Krzywinski & Cairo (one page; paywalled, so via the library) and the free chapter Wilke, Telling a story and making a point.
• Idea sequence after Heiss (2026), session 15.

The analyst you want to be

A good report uses these questions reflexively.

A HolmesCo report ignores them.

Your goal: be the analyst whose work a policy-maker can trust.

• This is the emotional capstone. Students have learned these habits through 10 weeks of practice. Name them explicitly and show that each came from a specific moment in the course.

Peer review briefing

• Recap
• Report structure
• Summary swap
• Peer review in science
• Common pitfalls
• Telling the story honestly

Peer review briefing

📋 For the application session

• Wrap-up

The process

In the application session:

1. Finalize your draft (20 min)
2. Review someone else’s report from a different group (40 min)
3. File 3–4 GitHub Issues using the updated checklist
4. Debrief and revise (30 min)

The review template is more demanding than Week 4’s — it now includes effect sizes, assumptions, reproducibility, and overstatement checks.

• Distribute review assignments now or at the start of the application session.

Wrap-up

• Recap
• Report structure
• Summary swap
• Peer review in science
• Common pitfalls
• Telling the story honestly
• Peer review briefing

Wrap-up

Any questions we missed?

Submit questions:

PollEv.com/geol

text geol to 07480 781235

• Continuation of the same Poll Everywhere Q&A activity opened at the start of the session — this is not a new poll, so don’t reset/archive it yet.
• Surface any questions that have come in since the early Questions slide, especially ones raised during today’s content.
• Once you’ve finished with this slide, archive (reset) the activity at the end of the session so questions don’t carry over.

Next time

Application session: “Review, revise, submit”

Bring a complete draft. Whatever is committed at minute 30 gets reviewed.

This is the last session. Make it count.

• Check the Q&A — surface any top-voted questions before finishing.
• The stakes are clear. Students should arrive with a near-complete draft.