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The can contain mementos or sub-collections. This makes getting metadata more complicated that getting it from Archive-It.
Each collection has a numeric identifier, such as 15003. The collection identifier exists at the end of the URL for that collection, e.g., https://webarchive.nla.gov.au/collection/15003. NLA collection 15003 has nine sub-collections.
Collection 13742 contains mementos instead of sub-collections. Our solution needs to be able to paginate and load additional content from the collection page. The "Show 10 More" button loads more content, but, to save on resources like RAM, we want to access the content without having to use a headless browser.
Fortunately, after some analysis with Chrome's developer tools, I've discovered that we can acquire a JSON representation of the collection via a URL like https://webarchive.nla.gov.au/bamboo-service/collection/13742 where we replace the last part of the path with the collection identifier.
Using ArchiveItCollection as an example, we will need to create another class that allows anyone to acquire this content via Python. Once this is done, it can be called from Hypercane or MementoEmbed, as needed.
The text was updated successfully, but these errors were encountered:
Collections at NLA are stored at https://webarchive.nla.gov.au/collection
The can contain mementos or sub-collections. This makes getting metadata more complicated that getting it from Archive-It.
Each collection has a numeric identifier, such as 15003. The collection identifier exists at the end of the URL for that collection, e.g., https://webarchive.nla.gov.au/collection/15003. NLA collection 15003 has nine sub-collections.
Collection 13742 contains mementos instead of sub-collections. Our solution needs to be able to paginate and load additional content from the collection page. The "Show 10 More" button loads more content, but, to save on resources like RAM, we want to access the content without having to use a headless browser.
Fortunately, after some analysis with Chrome's developer tools, I've discovered that we can acquire a JSON representation of the collection via a URL like https://webarchive.nla.gov.au/bamboo-service/collection/13742 where we replace the last part of the path with the collection identifier.
Using ArchiveItCollection as an example, we will need to create another class that allows anyone to acquire this content via Python. Once this is done, it can be called from Hypercane or MementoEmbed, as needed.
The text was updated successfully, but these errors were encountered: