Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 13 Next »

Important fields

frequency of updates => it will indicate how often the data is expected to change 
Last_modified => it will indicate the last time the dataset (resource) was changed, it is not only to monitor new data but also minor updates 
date of dataset => date to which data refers to. It has to change when new data comes to hdx but it does have to change for minor updates 

Thoughts

There are two aspects of data freshness:
 
1. Date of update: The last time the data was was looked at to confirm it is up to date ie. it must be examined according to the update frequency
2. Date of data: The actual date of the data - an update could consist of just confirming that the data has not changed
We should send an automated mail reminder to data contributors if the update frequency time window is missed by a certain amount. Perhaps we should give the option for contributors to respond directly to that mail to say that data is unchanged so they don't even need to log into HDX in that case, otherwise provide the link to their dataset that needs updating.
The amount of datasets that are outside of HDX is growing. I think we should try to handle this situation now. The simple but perhaps annoying solution is to send a reminder to users according to the update frequency (irrespective of whether they have already updated as we cannot tell).
Another way to do so is to provide guidance to users so that as they consider how to upload resources, we steer them towards a particular technological solution that is helpful to us eg. Google spreadsheet with update trigger, document alerts in OneDrive for Business, macro in Excel spreadsheet. I don't know if this is possible, but complete automation would be if they could click something in HDX that creates a resource pointing to a spreadsheet in Google Drive with the trigger set up that opens automatically once they enter their Google credentials.

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d) Compare age with frequency and define the logic: how do we define an outdated dataset

Number of Files Locally and Externally Hosted

TypeNumber of ResourcesPercentage
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%

Actions


Update frequency needs to be mandatory:  HDX-4919 - Getting issue details... STATUS
Investigate http get last modification date field - 60% in HDX have this according to UofV.

Classifying the Age of Datasets


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:

https://docs.google.com/document/d/1g8hAwxZoqageggtJAdkTKwQIGHUDSajNfj85JkkTpEU/edit#

Dataset Aging service:

https://docs.google.com/document/d/1wBHhCJvlnbCI1152Ytlnr0qiXZ2CwNGdmE1OiK7PLzo/edit


University of Vienna paper on methodologies for estimating next change time for a resource based on previous update history:
University of Vienna presentation of data freshness:
  • No labels