Introduction
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| Field | Description | Purpose |
|---|---|---|
| data_update_frequency | Dataset expected update frequency | Shows how often the data is expected to be updated or at least checked to see if it needs updating |
| revision_last_updated | Resource last modified date | Indicates the last time the resource was updated irrespective of whether it was a major or minor change |
| dataset_date | Dataset date | The date referred to by the data in the dataset. It changes when data for a new date comes to HDX so may not need to change for minor updates |
Dataset Aging Methodology
A resource's age can be measured using today's date - last update time. For a dataset, we take the lowest age of all its resources. This value can be compared with the update frequency to determine an age status for the dataset.
Thought has previously gone into classification of the age of datasets. Reviewing that work, the statuses used (up to date, due, overdue and delinquent) and formulae for calculating those statuses seems sound so we will use them as a foundation. It is important that we distinguish between what we report to our users and data providers with what we need for our automated processing. For the purposes of reporting, then the terminology we would use is simply fresh or not fresh. For contacting data providers, we must give them some leeway from the due date (technically the date after which the data is no longer fresh): the automated email would be sent on the overdue date rather than the due date (but in the email we would tell the data provider that we think their data is not fresh and needs to be updated rather than referring to states like overdue). The delinquent date would also be used in an automated process that tells us it is time for us to manually contact the data providers to see if they have any problems we can help with regarding updating their data.
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 |
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Number of Files Locally and Externally Hosted
| Type | Number of Resources | Percentage | Example|
|---|---|---|---|
| File Store | 2,102 | 22% | |
| CPS | 2,459 | 26% | |
| HXL Proxy | 2,584 | 27% | |
| ScraperWiki | 162 | 2% | |
| Others | 2,261 | 24% | |
| Total | 9,568 | 100% |
Determining if a Resource is Updated
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References
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