Authentic data in large repositories

Name:

Date:

Class:

Quick Questions

Shade in the A or B circle for each question.

1. Which describes authentic data?

A Real data collected by a trusted organisation

B Made-up data typed in to fit a task

2. Which is a large repository where authentic data is often stored?

A An unsigned post on a social media page

B A government open data portal such as data.gov.au

3. Before using a dataset, what should you check to identify the publisher?

A Which organisation collected and released it

B How many rows it has

4. A dataset was last updated in 2009. You need current data. What does this show?

A It may not be current enough for your question

B It is always the best choice because it is older

5. You are studying Victorian school attendance this year. Which dataset is more relevant?

A Rainfall data for Queensland farms

B Recent attendance data for Victorian schools

Match the Pairs

Draw a line connecting each item on the left to its match on the right.

Left column		Right column
1authentic data		AThe publisher is not identified and there is no date, so the data cannot be trusted as authentic.
2publisher: Bureau of Meteorology		BNames the trusted organisation that collected the data, so you can judge whether the source is reliable
3Check currency: the data should be recent enough to answer a question about today		CThe dataset is not relevant to the question and is out of date.
4A large repository is a big, organised store of datasets from trusted publishers that anyone can search		Dlast_updated: 2024-06-30
5source: unknown blog, no author or date		Esource: data.gov.au
6question: 2025 school enrolments \| dataset: fish counts from 1998		FReal data collected by a trusted organisation and kept in a large repository, such as the Australian Bureau of Statistics

Definitions

authentic data

Fill in the Blanks

Complete each line of code by filling in the missing parts.

SELECT title, publisher FROM datasets _____ publisher = 'ABS';

SELECT title FROM datasets WHERE year __ 2023;

SELECT title FROM datasets WHERE publisher = 'ABS' ___ year >= 2023;

SELECT _____(*) FROM datasets WHERE publisher = 'ABS';

SELECT title, year FROM datasets ORDER BY year ____;

Spot the Error

Shade in the letter of the line that contains an error.

Question 1

A repo = "data.gov.au"

B publisher = "random blog"

C trusted = ["ABS", "BOM"]

D print(publisher in trusted)

Question 2

A data_year = 2024

B age = data_year - 2025

C current = age <= 2

D print(current)

Question 3

A topic_ok = True

B years_ok = True

C ok = topic_ok and years_ok

D if not ok: print("Use it")

Question 4

A repo = data.gov.au

B size = 90000

C large = size > 1000

D print(large)

Question 5

A trusted = True

B current = True

C use = trusted and current

D if use print("Download")

Answer Key

Authentic data in large repositories

Section A: Quick Questions

A — Real data collected by a trusted organisation

B — A government open data portal such as data.gov.au

A — Which organisation collected and released it

A — It may not be current enough for your question

B — Recent attendance data for Victorian schools

Section B: Match the Pairs

authentic data → Real data collected by a trusted organisation and kept in a large repository, such as the Australian Bureau of Statistics

publisher: Bureau of Meteorology → Names the trusted organisation that collected the data, so you can judge whether the source is reliable

Check currency: the data should be recent enough to answer a question about today → last_updated: 2024-06-30

A large repository is a big, organised store of datasets from trusted publishers that anyone can search → source: data.gov.au

source: unknown blog, no author or date → The publisher is not identified and there is no date, so the data cannot be trusted as authentic.

question: 2025 school enrolments | dataset: fish counts from 1998 → The dataset is not relevant to the question and is out of date.

Section C: Definitions

authentic data — Real data collected by a trusted organisation and kept in a large repository; check its publisher, date and relevance to your question.

Section D: Fill in the Blanks

SELECT title, publisher FROM datasets
WHERE publisher = 'ABS';

SELECT title FROM datasets
WHERE year >= 2023;

SELECT title FROM datasets
WHERE publisher = 'ABS' AND year >= 2023;

SELECT COUNT(*) FROM datasets
WHERE publisher = 'ABS';

SELECT title, year FROM datasets
ORDER BY year DESC;

Section E: Spot the Error

B — Authentic data comes from a trusted publisher such as the ABS. A random blog is not in the trusted list, so the check would print False. Line 2 should be publisher = "ABS".

B — The age of the data is the current year minus the year it was published. Reversing it gives a negative age. It should be age = 2025 - data_year.

D — Data should only be used when it is relevant to the question (ok is True). The 'not' flips the logic and would use irrelevant data. It should be if ok: print("Use it").

A — The name of the repository is text, so it needs quotation marks. Without them Python treats data.gov.au as a variable name and gives an error. It should be repo = "data.gov.au".

D — An if statement needs a colon after the condition. Line 4 should be if use: print("Download").