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 |
last_modified | 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 datetime period | 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 |
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Date of update: The last time any resource in the dataset was modified or the dataset was manually 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.
Date Time period of data: The actual date or date range of the data within all the resources earliest start date and latest end date across all resources included in the dataset. This is dataset_date.
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The method of determining whether a resource is updated depends upon where the file is hosted. If it is hosted by HDX, then the update time last modified date is recorded, but if externally, then there can be challenges in determining if a url has been updated or not.
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Dataset Aging Methodology
Once we have an update time for the last modified dates for all of a dataset's resources and the last date the dataset was manually confirmed as updated in the UI if available, we can calculate its the latest of all of them, which we refer to as “last modified date” from here on. This is used to calculate the dataset’s age and combined with the update frequency, we can ascertain the freshness of the dataset.
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A resourcedataset's age can be measured using today's date - last update time. For a dataset, we take the lowest age of all its resourcesmodified date. This value can be compared with the update frequency to determine an age status for the dataset.
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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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