Lesson 2 of 12

Structured learning draft

Institutions with D3.js

In Data Journalism & Visual Storytelling, the way a learner handles institutions shapes how D3.js is used and evaluated. Institutions are rules and organisations shaping public action. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain institutions in the context of Data Journalism & Visual Storytelling.
Apply D3.js to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureSide-by-side comparison

Institutions: from context to evidence

Institutions connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.

Public need and evidence

Define the purpose, intended user and D3.js constraints.

frames

Institutions

Identify mandates, budgets and coordination constraints.

produces evidence for

Accountable outcome

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Institutions is credible only when the result can be traced back to its purpose, inputs and constraints. Institutions is the decision layer between the starting context and evidence that the result is fit for purpose.

Institutions are rules and organisations shaping public action. For D3.js, distinguish performing an operation from demonstrating that it suits the stated purpose. Identify mandates, budgets and coordination constraints. Record assumptions that could change the conclusion.

Apply institutions deliberately

State the Data Journalism & Visual Storytelling task and the decision it supports.
Prepare a small D3.js case with a known input and difficult boundary.
Identify mandates, budgets and coordination constraints.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

institutions evidence record
Review point	Evidence
Purpose	The specific D3.js outcome and intended user
Method	The institutions decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using D3.js before defining what institutions must achieve.
Checking only the easiest Data Journalism & Visual Storytelling example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Data Journalism & Visual Storytelling, complete a bounded D3.js task demonstrating institutions. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In Data Journalism & Visual Storytelling, which evidence best supports a institutions result produced with D3.js?

A polished D3.js output without its input The input, expected behaviour, normal and boundary results, and a limitation A screenshot showing the tool opened A claim that the method always works

Lesson summary

For Data Journalism & Visual Storytelling, institutions means: Institutions are rules and organisations shaping public action.
A credible D3.js result includes a checked boundary, not only a successful example.
The next lesson builds on this institutions evidence record.

Sources and further reading

Open Government Data ToolkitWorld Bank - accessed 2026-08-21
Digital governmentOECD - accessed 2026-08-21

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