Important fields
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Approach
a) determine the scope of our problem by calculating how many datasets are locally and externally hosted. Hopefully we can use the HDX to calculate this number.
b) Collect frequency of updates based on interns work
c) Define the age of datasets by calculating: Today's date- last_modified
d) Compare age with frequency and define the logic: how do we define an outdated dataset
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Thought has previously gone into classification of the age of datasets and reviewing this work, the statuses used (up to date, due, overdue and delinquent) and formulae for determining those statuses is sound. Hence, using that work, we have:
Update Frequency | Dataset age state thresholds (how old must a dataset be for it to have this status) | |||
|---|---|---|---|---|
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 |
References
Using the Update Frequency Metadata Field and Last_update CKAN field to Manage Dataset Freshness on HDX:
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