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Introduction

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  1. Date of update: The last time any resource in the dataset was modified or the dataset was confirmed as up to date. The ideal is that the date of update history time between updates corresponds with what is selected in the expected update frequency. This is last_modified.

  2. Time period of data: The earliest start date and latest end date across all resources included in the dataset. This is dataset_date.

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Update Frequency

Dataset age state thresholds

(how old must a dataset be for it to have this status)

Fresh

Not Fresh

Up-to-date

Due

Overdue

Delinquent

Daily

0 days old

1 day old

due_age = f

2 days old

overdue_age = f + 2

3 days old

delinquent_age = f + 3

Weekly

0 - 6 days old

7 days old

due_age = f

14 days old

overdue_age = f + 7

21 days old

delinquent_age = f + 14

Fortnightly

0 - 13 days old

14 days old

due_age = f

21 days old

overdue_age = f + 7

28 days old

delinquent_age = f + 14

Monthly

0 -29 days old

30 days old

due_age = f

44 days old

overdue_age = f + 14

60 days old

delinquent_age = f + 30

Quarterly

0 - 89 days old

90 days old

due_age = f

120 days old

overdue_age = f + 30

150 days old

delinquent_age = f + 60

Semiannually

0 - 179 days old

180 days old

due_age = f

210 days old

overdue_age = f + 30

240 days old

delinquent_age = f + 60

Annually

0 - 364 days old

365 days old

due_age = f

425 days old

overdue_age = f + 60

455 days old

delinquent_age = f + 90


Never

Always

Never

Never

Never

Live

Always

Never

Never

Never

As Needed

Always

Never

Never

Never

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